Building Lego Blocks required for Digital Twin manoeuvre are like building blocks required to implement use of Blockchain with trusted status updates of connected time-sensitive instances to maximise transmission. Have we encountered Block and Connection concepts elsewhere? Yes. Multi-Agent Systems are present in Lego Block Constructs, with each Agent representing one block component baseline unit contribution to Digital Twins. The convergence of Digital Twins and Blockchain is evident. Enterprises associated by function in operational sequences presents series of steps subdivided into blocks. Not only things/objects but also multi-agent units of work, process, verification decisions, outliers, feedback, metrics etc.
1. How will Digital Twins be used for faster decision-making and training?
As the Navy inserts more weapons and tools onto ships and aircraft, many needs have emerged for AI tools, especially given the service’s reliance on large data sets such as surveillance video and radar and sonar pictures.
“We tend to focus a lot on decision aids and how to use it to aid in decision-making speed. We look at it a lot of times on how to make sense of lots of data, whether it’s video processing or sonar processing or something along those lines.
An area we don’t talk enough about where there’s also very interesting opportunities is in training, and how do we speed up our training cycles. So if we get our acquisition cycle going very quickly, whether it’s building ships faster or putting new capabilities on ships, eventually we will be limited in its effectiveness by how fast we can train the crew and make them proficient.
Some of the use of AI is almost a training aid, that’s a whole other area of very interesting possibilities for us to deal with this kind of ever-changing world.”
2. What is an example of Digital Twin Application in the Fleet?
One early effort to create this kind of AI learning environment is through the use of the Aegis Digital Twin effort, which allows computers and consoles with the Aegis Combat System to be brought onboard a ship at sea and tap into actual radar and information feeds on the ship without interfering with the crew’s ability to navigate and fight the ship.
The consoles running Aegis with real-time data from the ship could be used to test out new capabilities that the Navy hopes to field and wants to solicit user feedback first, or it could be used for realistic training aboard the ship.
The Navy has said that adding artificial intelligence to this existing Aegis Digital Twin setup would allow the system to understand each user and track past performance, creating future scenarios based on proven weaknesses or areas that haven’t been tested previously rather than providing cookie-cutter training to all sailors.
Leaders are also eager to incorporate AI. Navy needs to buy ships and planes, but if it can’t also field enablers then it won’t win a high-end fight. One of the biggest enablers is digital supremacy. Who can turn knowledge into decisions most quickly.
In future combat, we need systems to get to the right decision most quickly. And what enables decisions? Turning a lot of data over to algorithms to crunch it to help give a decision – what most people call artificial intelligence.
3. What AI actions must be taken to improve the way Navy operates
Concept of turning data into the right decision more quickly – or more importantly, pointing out when something is going south – is incredibly important. So artificial intelligence … will help speed decisions.
Advances in information as warfare would improve “the ability to command and control a fight, the ability to sense an environment. ability to direct fires, integrate fires. That’s information as warfare, and that’s what we’re working on.”
But the Navy can’t just incorporate new technology for the sake of modernising; it needs to fully rethink how it conducts missions and how it buys new technologies, in the wake of this new digital warfare world.
We need ability to go faster, being able to think about things differently, how we approach things, how we buy things, how we require things and how we get after the fight.
“We can keep improving the way we’re doing business today, but if we need to do business completely differently in order to move at speed and scale, then we’ve got to figure out what that different is.
4. How is AI being used in ongoing wargames?
Navy is expanding its attack submarine war game strategy to further emphasise enhanced “spy” missions like intelligence, surveillance reconnaissance missions to quietly patrol shallow waters near coastline - scanning for adversary enemy submarines, surface ships and coastal threats.
Improved undersea navigation and detection technology, using new sonar, increased computer automation and artificial intelligence, enable quieter, faster movements in littoral waters where enemy mines, small boats and other threatening assets often operate.
Virginia-Class submarines are engineered with a “Fly-by-Wire” capability which allows the ship to quietly linger in shallow waters without having to surface or have each small move controlled by a human operator.
With “Fly-by-Wire” technology, a human operator will order depth and speed, allowing AI tools to direct the movement of the planes and rudder to maintain course and depth.
The ships can be driven primarily through AI code and electronics, thus freeing up time and energy for an operator who does not need to manually control each small manoeuvre. Previous Los Angeles-Class submarines rely upon manual, hydraulic controls.
This technology, using upgradable and fast-growing AI applications, widens the mission envelope for the attack submarines by vastly expanding their ISR potential. Using real-time analytics and an instant ability to draw upon an organize vast data-bases of information and sensor input, computer algorithms can now perform a range of procedural functions historically performed by humans to increase speed of manoeuvre and an attack submarine's ability to quickly shift course, change speed or alter depth positioning when faced with attacks.
5. What is a good example of where digital twins are being used at the operations level?
A good example of where digital twins are being used at the organisational level is in military manoeuvres. By creating a digital twin of the mission space, Marines can get powerful, real-time insight into combat workflows.
Using sensors to monitor troops and coordinate equipment, digital twins offer a better way of analyzing processes and alerting commanders at the right time when immediate action is needed. As a result, operational wait times can be reduced and workflow can be improved, decreasing operational costs and enhancing mission outcomes.
6. Variation exists is in how digital twins are implemented?
Digital twin technology can be used in new and mature ways, integrating sophisticated sensors, AI, and machine learning, to solve the biggest organisational challenges. In order to maximise their usefulness, digital twins need to be powered by high-performing databases that can pull together and process many data sets in real-time.
Where digital twins offer new and remarkable possibilities is at the organisational level in the built environment. Implementing digital twins offers the potential to create beneficial outcomes not only for administrators but also for the manoeuvre troops. In this way, digital twins can be used to take a troop-centric approach and then look at problems and context, and finally adding network systems and connected devices to try to solve big problems and create long-term value.
For organisations that already use advanced networks, digital twins are the next step along the digital journey. Digital twins can be used to improve efficiencies, optimise processes, detect problems before they occur, and innovate for the future. If your organisation is interested in producing better operational outcomes digital twins must be explored
7. How do Agent based systems contribute to operational areas like logistics?
Integration of the Simulation exercise that we have carried out at in different logistics scenarios validated that the agent-based technology is viable in situations where the maintenance planning problem is constantly changing e.g. project driven production, and requires frequent and continuous re-planning.
In these Readiness Simulations the collective aspects of the agent technology have been exploited. At the same time we have identified a great potential of the technology in situations where the planning problem is characterised by complex processes but it features some of the internal logistics.
In the case of the high volume production/maintenance availabilities, where not only collective aspects of agent simulation technology have been used but also the integrative capabilities of agents have been exploited e.g. integration of the linear programming heavy-duty solver.
In simulations where the planning metrics are widely distributed and not fully available the agent technologies provide an robust integrative and distributed planning framework for supply chain administration and virtual production/maintenance organisations formation.
8. What are the prospects for new digital enterprises?
Integration of virtual enterprises must be developed and their use must be populated through examples and application experiences. The objective is to develop and validate a step forward in the state of the art of Digital Architectures for Enterprise Integration.
Objective consists of architecture section selection to describe and present all the necessary activities to establish, carry out and complete an enterprise integration programme for any kind of enterprise.
In the case that an enterprise fails to perform its duties, the Virtual Enterprise must be reconfigured to replace the failing enterprise with another one. To support this functionality it is nice to have a distributed digital process plan/model tool to allow for re-planning and re-scheduling of logistics processes.
To support function of Virtual Enterprise-- independent of Virtual Enterprise size there is a need for a Virtual Enterprise coordinator. to monitor distributed logistics process Job Status and comparing it to Virtual Enterprise plans as described in the contracts.
9. What requirements and components must be put together by digital standards teams?
Any kind of proposal for an enterprise integration reference architecture can be evaluated under certification criteria.
Strong reliance on standards will contribute to facilitate the interfacing of existing applications with the virtual infrastructure, but unfortunately not all classes of information that need to be exchanged among virtual agents are covered by existing standards.
Initiatives of application agent groups contribute to facilitate this process. In general, it is necessary to develop some interface/mapping layer, at each enterprise, to adequately have this enterprise interacting with virtual infrastructure.
10. How does Blockchain Tech Utilise Cooperative Agent Structures?
Here we describe single Phases of cooperation life-cycle on Enterprise-to-Enterprise level searching of the possible product support collaborators. First, agents have to contact possible partners. There is wide field for future research in the domain of automatic search cooperation and contacting possible partners.
This Blockchain approach ensures the trustworthiness of the partners transferred from real-life to the agents cooperation. Each agent is equipped by the addresses and the security certificates and every partner can be authenticated using standard key methods. Every agent can be connected to many partner agents according to defined internal cooperation rules.
Once the agents are connected together, each agent provides the list of available product support capabilities to partners. It is possible to propose different capabilities to different partners. During this phase agents form basic cooperation network, receiving information suitable for effective collaboration in the next phases. During the life-cycle of the cooperation, agents subscribe information of the changes on product support resources on already established platforms.
11. How to balance transition from produce many parts from a few orders to producing a few parts from many orders?
“It’s definitely one of the biggest roadblocks right now. “Even if you’re fast, it usually takes significant block of time to prepare a quotation for an order. There’s also a lot of ‘communication “ping-pong” at the beginning: someone sends a request, you check if the part is printable, it’s not, so you request changes back and forth, and it just goes on like that.”
Fortunately, you can avoid all that communication “ping-pong” by automating your quotations and printability checks. Our Enterprise Platform is designed to do just that. In addition to giving you direct access to our network of print parts suppliers, the platform can also evaluate models for pricing and printability, include side-by-side comparisons of internal 3D printers with those in your on-demand prototypes network
12. How do AI tools provide complete visibility on manufacturing/service operations,?
Multi agent systems can be used also for a digital simulation and modeling of the production process or the supply chain, where they easily simulate an independence of involved parts. These tools can help to answer non-trivial tasks – how changes in single component will affect the production process or supply chain as a whole.
AI applications also help us improve the accuracy of our work orders; engage in more efficient production scheduling; enable interaction with our diverse vendor supply chain and reduce logistics delays for parts.
We have also noticed most legacy applications were designed for a enterprise-centered local operation and to be operated by agents. In order to have these applications supplying information to or consuming information from the virtual network, it is clearly necessary to extend their functionality.
13. Why is Interoperability among enterprise applications such a big challenge for supporting rapid formation of virtual scenarios?
in response to new operational opportunities it’s important to consider that each enterprise may have its own way of doing business. The level of information sharing among virtual agents is likely to either change of the trust level among partners or with adjustments to configuration in time.
So flexible virtual coordination and information visibility rights definition mechanisms are required to support both the autonomy and change order properties in behaviour of virtual agents.
Multi-agent solutions exist for low-level scheduling or control systems as well as product-configuration and quote phases to be used for short- and long-term production planning and supply chain administration.
14. What are the determinants of agent behaviour for Enterprise levels?
Multi-agent systems on intra-enterprise level and extra-enterprise level are independent in the digital population point of view. Agents used on intra-enterprise level are operating inside an enterprise represents many units or processes in the unit. On extra-enterprise level, whole unit is represented by a single agent, providing all abilities and services, available in the company.
If agents on both levels are used, a special Digital Twin agent can exists that bind both levels together. For digital application purposes, both levels can be modeled together as a Digital Twin to study and improve their abilities.
15. How should field-level job sites incorporate AI?
Advances in artificial intelligence have given rise to behavioural technologies capable of performing tasks that traditionally required intelligence of commanders in the field. Realising end-to-end digital enterprises, automating tasks and processes, and making smarter, faster decisions all require next frontier of technologies to transform the way agents and machines interact.
Must support realistic virtual Job Sites to test agent behaviours. The architecture should support construction of very responsive physical and behavioural mission space and stand in for the challenging real world counterparts in which agents are designed to function. This is a particular strength of an architecture that incorporates state-of-the-art simulation capabilities.
16. How will advances in AI shed light on how deployment decisions will affect forces in the long run?
By analyzing historical trends along with real-time data, AI tools could show how a unit’s readiness would change if it were, for instance, moved to a new location or given additional resources
Marines are also building a separate AI system that ranks course of action plans based on those extrapolations, which could one day be merged with the readiness system.
“You integrate that all together and you get a full view of readiness across your force. Now you can really make some data-driven decisions.”
The next stage of the effort will include parts of the Marines’ aviation and logistics units, bringing about half branch into the purview of the program. With that additional data, the AI would further refine its processing rules to deliver better results.
So artificial intelligence is tasked with managing the particular deployments of troops in battle, moving them around in new and unexpected ways.
17. What are some practical applications of AI that could be fielded soon?
One way that future developments in AI might manifest is by looking at a place where AI already manages workforce inventory-- like a warehouse stocking system, a process in which items are unloaded wherever there is space in a warehouse and then scanned into a computer system than can track where the item is located.
When it comes time to retrieve an item for delivery, the same computer system directs warehouse workers to the most efficient route for finding the item, which could be stowed throughout the warehouse.
When modeling the warehouse system, it is interesting to consider how AI, given the same objectives as a commander, might organise and direct forces to achieve them.
“Why would an AI allocate forces in distinct areas of the battlefield? It could intermingle them and manage them at a granular level. Its categories are way more numerous, in the way that warehouse AI manages categories at the shelf level.
Instead of distinct groupings of armour, air support, infantry, and artillery, a system run by artificial intelligence and managing a battle could coordinate a single helicopter with a pair of howitzers and an infantry platoon, directly grouping each in the same way that a warehouse worker finds an assortment of items to place into the same package.
18. How do Multi-Agent Models contribute to AI development?
One of the main underlying trends in Training development is the focus on agent models, protocols, and mechanisms to support the collaboration of pre-existing entities in distributed real-world scenarios, be it among organisations, among Troops, or among Troops and organisations.
It’s important to continually assess approaches to systems training/engineering, including use of agent models, simulation results, artificial intelligence techniques, and tools supporting training process so it is possible to stay ahead of demands for new and upgraded weapons systems.
Using agent models is not a new concept; however, digital engineering will address long-standing challenges associated with complexity, uncertainty, and rapid change in deploying defense systems. By providing for more agile and responsive development, digital training/engineering supports engineering excellence and provides a foundation for mission success in the future.
Realising end-to-end digital enterprises, automating tasks and processes, and making smarter, faster decisions all require next frontier of technologies to transform the way agents and machines interact. Advances in artificial intelligence have given rise to behavioural technologies capable of performing tasks that traditionally required intelligence of commanders in the field.
19. What multi-agent technologies could speed AI development?
Covering all requirements of modern military logistics enterprise simulations is not easy problem. We propose two agent-based technologies for logistics support on two different levels: intra-enterprise and extra-enterprise level-- can be used simultaneously or together.
The standard configuration consists of several independent systems linked to the virtual organisation by agent-based decision support technology in various fields of logistics – maintenance planning, supply chain decision-making models, simulation, extra-enterprise access, etc.
Although established architectures have many good points, all these architectures can be improved, since they have not completely generated the necessary digital modeling techniques and adequate execution tools for the different kinds of enterprise architectures and specialised tools focus on necessities of every type of enterprise activity.
20. How does AI contribute to digital engineering tech like generative design?
Generative Design is new approach to engineering set to transform the potential of manufacturing over the next decade. It’s a process that enables engineers using digital tools to define an engineering problem, and solve it over and over again by an adaptive artificial intelligence program, yielding different results each time.
if you pool enough computing horsepower, your network is capable of completing many iterations of very complex tasks very quickly. Questions about the potential for many designs used to be fairly straightforward. Design teams have a finite amount of time and money at their disposal, and can’t afford to prototype more than a few designs—let alone thousands.
But what are these constraints costing you, in terms of the ideas that never get tested and the solutions that never get prototyped? Are there unexplored methods of building your product that might be lighter, faster or cheaper?
21. Why is the power of parallel computing going to help us test more ideas and look at more concepts in a shorter period of time?
The “secret sauce” is an AI platform that has been “trained” to create solutions to engineering problems. Unlike human engineers—who first design a solution, then determine how to build it, then prototype and test its properties—generative design is capable of carrying out all three of those steps simultaneously.
The process requires human engineers to define the problem by using established systems to lay out basic specifications for the component that needs to be designed. “You don’t start with the geometry. You start where it attaches to other components and how.”
After that, engineers further refine the design parameters, specifying load requirements, deflection, rigidity, material preferences, cost of production, weight requirements and even manufacturing methods, attention to detail is critical during the process of defining problem.
“If you input junk you’re going to get back junk, essentially. “But if you actually specify the requirements early and set up to solve the right problems, that’s when you’ll see the benefits of the tools.
22. How is Marine Corps is experimenting with AI to improve the way it deploys its forces?
The Marines built a tool that crunches data on personnel and equipment to measure how prepared individual battalions are for combat and spot potential weaknesses years in advance.
Today planners rely on spreadsheets, whiteboards and basic applications to track readiness and manage forces, but artificial intelligence can offer them a better understanding of the resources at their disposal and the long-term effects of the decisions they make.
The tech crunches both structured and unstructured data from multiple force management applications to create a real-time image of how prepared each unit is for combat. The tool specifically aims to build a five-year management plan for the Marine infantry battalions.
Allocating the service’s resources is an imperfect science. Leaders map out deployment strategies years or even decades in advance, but situations will invariably arise that throw a wrench in those plans.
Planners are constantly forced to “reshuffle the deck” as crises flare up in different places and figuring out which units to move around is a complicated process. Numerous factors—training, deployment history, equipment readiness and others—affect how prepared a group is for a given situation.
23. What is the strategy AI tools use to impact battlefield planning?
Tool has two primary functions: It flags the units that are most ready for action and explains why others come up short. Armed with that knowledge, commanders can proactively train and invest in less prepared groups before they fall even further behind.
“A lot of times Mairnes only invest more when the problem arises. Now they can see it ahead of time and say ‘OK, we’re going to take action now to prevent that from occurring.’”
The tool sheds light on how deployment decisions will affect forces in the long run. By analyzing historical trends along with real-time data, the tool could show how a unit’s readiness would change if it were, for instance, moved to a new location or given additional resources.
Marines are also building a separate AI system that ranks course of action plans based on those extrapolations, which could one day be merged with the readiness system.
“You integrate that all together and you get a full view of readiness across your force. Now you can really make some data-driven decisions.”
The next stage of the effort will include parts of the Marines’ aviation and logistics units, bringing about half branch into the purview of the program. With that additional data, the AI would further refine its processing rules to deliver better results.
24. How does AI manages field-level battlespace workforce inventory?
Instead of distinct groupings of armor, air support, infantry, and artillery, a system run by artificial intelligence and managing a battle could coordinate a single helicopter with a pair of howitzers and an infantry platoon, directly grouping each in the same way that a warehouse worker finds an assortment of items to place into the same package.
An example of AI application is a warehouse stocking system, a process where items are unloaded wherever there is space in a warehouse and then scanned into a computer system than can track where the item is located.
When it comes time to retrieve an item for delivery, the same computer system directs warehouse workers to the most efficient route for finding the item, which could be stowed throughout the warehouse.
When modeling the warehouse system, it is interesting to consider how AI, given the same objectives as a commander, might organise and direct forces to achieve them.
“Why would an AI allocate forces in distinct areas of the battlefield? It could intermingle them and manage them at a granular level. Its categories are way more numerous, in the way that a warehouse AI manages categories at the shelf level.
25. How is artificial intelligence is tasked with managing troop deployments to move around in new and unexpected ways?
“Anytime we’re on the road, our job, maintenance wise, is to provide safe and reliable jets for the pilots to accomplish their mission. Every new location presents a different challenge in how we get the job done, but the end goal for providing a safe jet for a pilot never changes. What does change is the environment in which we operate in.”
“Every exercise you go on is different, and it can be hard to start off. It could be not having the parts we need on hand, or not knowing how the base operates to get the support we need. Over time you figure out how to acquire some of that on site, what to bring along yourself and how to solve a problem before it becomes one.”
Here we consider how AI systems could be useful to a typical work order job of launch and recovery of aircraft, engine maintenance and servicing of life-saving equipment-- just a few of dozens of tasks Troops are expected to accomplish within a full day.
26. What obstacles does the AI sector face to gain traction with military?
Even while the groups and organisations hyping artificial intelligence solutions popped up everywhere at the expos with promises to create the next battlefield advantage using next generation weapons, gear, or satellites. The term artificial intelligence splashes the headlines with promises that we’re moments away from revolutionising the battlefield.
It’s frustrating. The special AI “task forces” and their massive budgets are great, but it’s time to get honest about the rest of the military. Ask any Marine their opinion of how things run on a daily basis and you will hear complaints about lost orders, broken gear, and outdated technology.
Bottom line: all those flashy AI applications being touted as perfect for Marines use are not going to run on the outdated infrastructure on which a majority of the military still operates.
That doesn’t mean that AI isn’t a good fit or shouldn’t be pursued. But it does mean that AI success requires a force readiness approach. First, AI isn’t new and it isn’t new to the military.
Marketing hype around the term has experienced a surge lately but the fact that something wasn’t tagged as artificial intelligence historically does not take away the fact that it was actually AI.
Despite the hype, AI is simply a field of science that trains systems to perform some human task through learning and automation. There are varying degrees of sophistication but most of the mining, network assessment constructs and mapping technology used over the past decade or more have all been forms of AI.
Weapons systems and combat vehicles have been leveraging AI for many years as well. So don’t let the noise change the focus from the mission need.
There are varying degrees of complexity but most of the data mining, network engineering and mapping technology used over the past decade or more have all been forms of AI. Weapons systems and combat vehicles have been leveraging AI for many years as well.
27. What developments need to happen to improve AI collection of data?
Marines on the front lines need their supporting forces to be trained and armed with the appropriate technology to support the advances being operationalised on the battlefield. If we look specifically at the intelligence arena, the vast majority of military intelligence analysts are still using the same products and systems from 10-15 years ago.
Efforts around collecting intelligence are ripe for avances, but what about the Marines that have to sift through and make sense of that additional data? How has their training changed to account for a more technologically advanced battlespace? How do products and solutions integrated requirements and workflows with real time information truly augment their efforts?
The majority of data mining and visualisation tools on the market have flashier interfaces than we saw a decade ago, but the true sophistication of what the vast majority of Marines have been offered doesn’t really reflect the decade of advancements seen in the commercial market.
28. What are some examples of operational value provided by AI?
You don’t have to be a part of a high profile AI initiative to find value in the technology for nearly all areas of the military. We need the whole force to have the technical advantage on the battlefield and that means AI must become a force readiness initiative.
It’s all about augmenting human efforts across battalions, regiments and divisions to raise the readiness levels of the entire force. Marines inside the wire should have the knowledge, technical skills and agility to support all of the operations and technology our troops outside the wire are running.
Then there’s the applicability across all military systems. An “AI watchman” could prevent ships from colliding with one another since the computers are “constantly looking at sensor data and is making sense of the environment and the situation.”
“There is that safety aspect of using artificial intelligence to augment the level of capability and intelligence available on ships, on tanks, in aircraft, all over, where you almost have an embedded AI technician be part of every military asset.
29. What are some obstacles to AI use of data?
Programs find themselves overwhelmed by the sheer volume of data, putting the program at risk, if data spends too long in the review state, missing the contractual response deadline.
Without a vector check from DoD, the contractor assumes a conditional acceptance and moves forward, assuming they are on the right track. This could lead to a host of problems, including a possible schedule slip.”
The team is now spreading the proven results of tools and showing individual operational units can use the tools to suit various purposes. Versatility is inherent in the review and content-sharing applications, so new users usually recognize ways to streamline the bulk of documents that shuttle through an acquisition enterprise.
The objective is simple: information dominance through the creation, review, approval and dissemination of data. If you have unrefined, makeshift processes, this won’t work for you, at least not yet. Business processes work more efficient with automation so there are now options if your workflow is functional, but uses outdated tools or requires intense use of workforce capital.
30. How can Blockchain contribute to field-level work order accuracy?
Every agent in the “Blockchain” can see, through comment tracking, how data changed to capture the correct info and meet requirements.
“The advantage of the utilities lies in the ability to collaborate on existing work, review past work and evolve the system architecture to meet changing needs. These tools provide value now, and value in the future by giving agents access to program work performed
This fidelity, coupled with the capacity to short-circuit future process complications, makes work order content-sharing applications ideal for long-running, complex weapons systems sustainment program offices looking to limit volume of work spent on acquisition schedule.
Blockchain bucket-brigade content for sustainment execution, operational manoeuvre etc. communication interface is evaluated in the implementation of space-time parallel applications for massively parallel machines.
The simplified version of in-time contract content work order rules is valid in time-dependent problems, and that it can be implemented as the form of the bucket-brigade communication with simple computations.
31. What are some effective strategies for moving toward large scale computing?
Must evaluate performance of several configurations by the use of interface for the bucket-brigade communications. On the basis of robot/agent for sustainment business operation performance measurements or any other practical applications,.
There is way to parallelise operations dynamically on a “Blockchain” while maintaining both decentralisation and security. Looking on the internet, no one spelled out this design in particular. It might actually help with scaling.
In contrast to just breaking up the whole system into smaller parts like you do in a bucket brigade, this design provides a much higher adaptability to the current utilisation of the communication network, however, does not improve the storage consumption on each node like breaking up into small parts does.
There’s no reason not to implement this strategy on top of current practise for increasing flexibility and weapons system sustainment work order transaction throughput. Another factor to consider is that many of the top “Blockchain” projects are still utilising proof of work.
32. Why is it difficult to change agent consensus rules process?
Changing consensus rules requires some time. Programs find themselves overwhelmed by the sheer volume of data, putting the program at risk, if data spends too long in the review state, missing the contractual response deadline.
Without a vector check from DoD, the contractor assumes a conditional acceptance and moves forward, assuming they are on the right track. This could lead to a host of problems, including a possible schedule slip.”
The team is now spreading the proven results of tools and showing individual operational units can use the tools to suit various purposes. Versatility is inherent in the review and content-sharing applications, so new users usually recognize ways to streamline the bulk of documents that shuttle through an acquisition enterprise.
The objective is simple: information dominance through the creation, review, approval and dissemination of data. If you have unrefined, makeshift processes, this won’t work for you, at least not yet.
Business processes work more efficient with automation so there are now options if your workflow is functional, but uses outdated tools or requires intense use of workforce capital.
33. How do agents work together to execute Blockchain contracts?
An agent can make mutually exclusive low-commitment offers to multiple agents. In the case more than one accepts, the agent can backtrack from all but one so agent can address the other parties in parallel instead of addressing them one at a time and blocking to wait for an answer before addressing the next.
For example, if an agent wants one particular contract, it can offer that contract to several parties with meaningful commitment instead of no commitment at all that would be strategically meaningless. Load balancing is crucial for parallel applications since it is representative of good use of the capacity of the parallel processing units.
Here we look at applications putting high demand on the parallel interconnect in terms of throughput. Examples of such applications are compression applications which both process important amounts of data and require a lot of computations undesirable for most parallel architectures. The problem is exacerbated when working with heterogenous parallel hardware. This is the case when using a heterogenous cluster to execute parallel application
Reinforcement consists of redistributing bids made between subsequently chosen rules. The bid of each winner at each time-step is placed in a "bucket". A record is kept of the winners on the previous time step and they each receive an equal share of the contents of the current bucket; fitness is shared amongst activated rules. If a reward is received from the work space communications then this is paid to the winning rule which produced the last output. Each rule is much like the middleman in Bucket Brigade Blockchain.
34. What are some anticipated difficulties to wide-scale Blockchain application?
As we have outlined in this report, Blockchain is an emerging technology for decentralised and transactional supply line connection monitor sharing across a large group of supplier Network intersections. It enables new forms of distributed supply line connection monitor networks, where agreement on shared states can be established without trusting a central integration point.
A major difficulty for architects designing applications based on blockchain is that the technology has many configurations and variants. Since blockchains are at an early stage, there are limited number of product support success stories or reliable technology evaluation available to compare different blockchains.
35. What improvements does Blockchain bring to supply chain management for manufacturing and parts suppliers?
Blockchain is the digital and decentralised exchange of value technology that records all transactions without the need for an intermediary. Businesses aim to manufacture goods — whether end products, solutions or precision parts of the highest quality, for the best price, with the greatest technical support, and according to agreed timelines.
Blockchains enable the creation of intelligent, embedded and trusted programme supply line connection monitor, letting suppliers build terms, conditions and other logistics parameters into contracts and other transactions. It allows suppliers to automatically monitor agreed upon value figures, delivery times and other enabling conditions, and automatically negotiate and complete transactions in real time. This impacts cost/benefit of work orders, maximises efficiency and allows for multiple avenues leading to supply line connection monitor.
36. Why is it important for Blockchain systems to store work orders?
A blockchain is a shared, distributed, secure supply line network connection monitor that every participant on product support service routes can share, but that no one entity control. In other words, a blockchain is a supply line connection monitor that stores work order routing records. The routing intersection is shared by group of service route supplier participants, all of whom can submit new records for inclusion.
Blockchain records are only added to the supply line connection monitor based on the agreement, or consensus, of a majority of the supplier group. Additionally, once the records are entered, they can never be changed or erased. In sum, blockchains record and secure supply line route dispatch information in such a way that is becomes the agreed-upon record for stakeholders of important contract terms and enabling conditions.
Smart contracts can be instantly/securely sent and received over the Blockchain Network reducing exposure/delays in back office dispatching. As an example, oversight of Purchase Requests could be securely implemented with greater transparency and also potential battlefield applications messaging system could be leveraged during instances in which troops are attempt to communicate back to HQ using secure, efficient and timely logistics system.
Built-in supplier incentives to assure the security of every transaction and asset in the blockchain allows routing technology at intersections to be used not only for transactions, but as a product registry system for recording, tracking and monitoring all assets across multiple value suppliers. This secure information can range from information about parts or contract work-in-progress such as product specifications and purchase orders.
37. Why are Blockchain connections more reliable than traditional technologies?
Because blockchain is based on shared consensus among different suppliers, the information on the blockchain is reliable. Over time, suppliers build up a reputation on the blockchain which demonstrates their credibility to one another. Furthermore, because trust can be established by the supply line connections, third party monitor of routing intersections between two suppliers will no longer be necessary.
In order to establish sufficient trust to become involved in a blockchain supply line connection monitor, the motives and goals of DoD and involved suppliers must be clear. The reputation of the participants becomes transparent and grows over time. It is important that suppliers in the routing market space can trust each other in order to share information and increase efficiency in shared processes.
Blockchain networks also open the door for machine-to-machine transaction capabilities to enable the transformation of a traditional supply line connection, where work order transactions and contracts must be maintained by each DoD dispatcher agent in market interaction with suppliers.
38. How do agents work together to solve distributed problems?
Distributed problem solving concentrates on competence; as anyone who has played on a team, worked on a group project, or performed on a football team can tell you, simply having the desire to work together by no means ensures a competent collective outcome.
We have described Single Phase of cooperation life-cycle on Enterprise-to-Enterprise level search for possible product support collaborators. First, agents have to contact possible partners. There is wide field for future research in the domain of automatic searching and contacting possible partners.
This approach ensures the trustworthiness of the partners transferred from real-life to the agents cooperation. Each agent is equipped by the addresses and the security certificates and every partner can be authenticated using standard key methods. Every agent can be connected to many partner agents according to defined internal cooperation rules.
Once the agents are connected together, each agent provides the list of available product support capabilities to partners. It is possible to propose different capabilities to different partners. During this phase agents form basic cooperation network, receiving information suitable for effective collaboration in the next phases. During the life-cycle of the cooperation, agents subscribe information of the changes on product support resources on already established cooperation.
39. What network Marketing problems can be fixed with field-agent mechanisms?
Establishing field agents for product/process design creates agent-based tools to construct market places among members of a Distributed design team to coordinate set-based design of a discrete build product. Designers of components are empowered to "Buy" and "Sell" desired characteristics engineers are motivated to assume.
Here we describe the entities interacting in the market space and outline the market space required to make trade-off decisions on each characteristic of a design. Agents representing each component "Buy" and "Sell" units of these characteristics. A component that needs more latitude in a given characteristic, i.e. more weight can purchase increments of that characteristic from another component, but may need to sell another characteristic to raise resources for this purchase.
40. Why Is the position/order tracking tag so important in Blockchain Systems?
In network marketing, each participant essentially has one core asset- the position/order tracking tag in the network recorded with fidelity. Tokens “locked” into the system provide privileges in using the system as intended. It could be revenue share in a typical marketing scheme or it could be a discount on risk charged by the system charges for its services.
In Distributed problem solving, we typically assume a fair degree of fidelity is present: the agents have been designed to work together; or the payoffs to self-interested agents are only accrued through collective efforts; or engineering relationships between units has introduced disincentives for agent individualism; etc.
41. What needs to happen with Blockchain to enable advances in Digital Twin application?
Most block chain service providers offer only narrow class of use cases. Site Visit Executive looked at the problem and collapses the apparent wide-range of Blockchain services into couple of topics: supply line optimisation and streamlining network marketing processes.
Before DoD has Blockchain in some form, no new “Digital Twin” model can work. Spatial navigation became possible after DoD got GPS sensors in its pockets; Situational awareness became possible after DoD got the sensors connected.
Not side products but instead secondary products of Blockchains have broadly adopted tokens and develop into applicable results such as “Bucket Brigades”.
Bucket brigade systems are a tool to build robust simulated robot control systems. This choice is sufficient to achieve adequate levels of performance for a variety of behaviours. The parallel implementation of the bucket brigade system is sure to speed up the training process and implement robotics controller.
42. How do Blockchain Systems contribute to task learning?
Bucket brigade systems provide guidance shortening the number of cycles required to learn task rules using only a few training examples starting with randomly generated classifiers. Bucket brigades compensate for lack of time to deploy field-ready tech.
Most DoD Leaders associate bucket brigades with disasters, but they are mostly used in normal conditions to load or unload something, for example a truck of ammo supplies. Another example of Bucket Brigades is warning beacons,
In every case, Bucket Brigades compensate for the lack of technology, temporary or permanent. Information transfer is compromised by technological shortcomings of all networks not able to allow for multiple interpretations or deliver the information with fidelity.
43. How does multi-agent approach provides a specific alternative to known system model tech for simulating manoeuvre process?
We set up experiment involving “Digital Twin” robots learning to work together: one robot ideally handing off Tokens to the other, which in turn carries them to a final destination. Discounting rewards results in the first robot receiving significantly less reward than the second one.
Tokens are transferred between Digital Twin agent pairs in the Bucket Brigade, without any centralised system watching or authorising interactions.
In a tech set without Bucket Brigades, now a stronger than ever trend to automate with machine learning, each node is as stupid as a box of rocks, unless it is perfectly prepared for the job. Each element has to be precisely calibrated or the system is in major trouble. Even if some aperture is allowed, an error propagation is a strong and adverse phenomenon.
44. Why is it necessary for Blockchain to collect info on the frequency of reward?
Blockchain solutions have information about how many times a resource was used as reward and for what segment of equipment, so you can see in what operational theatre the discount effect is greatest. Robots can write and pass comments on it.
Tokens aren’t anything new. Each of us use incumbent tokens daily without even noticing: keys, tickets, receipts, reservations, network certificates etc. Ordinary tokens are already vital, but they are not very smart. More importantly, they are expensive to issue and even more expensive to maintain, with a large portion of barriers to implement system associated with security.
45. How can Blockchain change the way of doing business and consumption of operational resources?
As many Blockchain bridge connections are manifest in operations, tokens promise new productive economic scenarios when random “Digital Twin” token pairs can become mutually usable in changing conditions as a result of more reasonable inputs so many essential operational parameters can be articulated with greater precision.
On one hand, smart decision-making can be rewarded immediately in tokens. On the other hand, it always costs some number of some tokens to do something. So DoD operatives either make an economically-responsible decision or do not contaminate the feed, abstaining from any involvement.
As open token-carrying platforms become more widespread, anyone can maintain an unlimited number of token output. Soon, piles of tokens will represent everything that can be counted in an economically meaningful way.
Within token-enabled supply chains, every participant seamlessly contributes to the quality automated event flow. Most economic acts can be done through token exchange, issuance or redemption. When tokens circulate, things you normally run operations, and procurement on happen “by themselves”, with much less overhead than normal.
46. How do Blockchain transfer mechanism address changing scenarios?
When being passed continuously between units, tokens be very, very smart when it comes to changing scenarios.
Passing Tokens from one unit in the Bucket Brigade to another is a fundamentally local event. No one else but the units party to this exchange is required and if we consider distributed token-carrying platform connection bridges between them as free-access, self-maintaining ownerless entities.
Examples of that ubiquitous miracle of local interaction are everywhere: The great complexity of physical phenomena troops encounter is the result of endless iterations of similar “local acts”: circles on the water don’t need a concentric dispatcher.
Bucket Brigade Junctions in token interactions can be much smarter: since the constructs can also bear an often-needed note of context rather than the iron extremes present in automated operations.
But what about more complex things than just transfer of value, such as level of local interaction relevance under changing operational conditions where quality of information is not possible to quantify..
47. How can AI simulators enable commanders to lead virtual troops?
Some of the advanced weapons can’t be demonstrated where just anyone can see them in action, thus revealing our tech to adversaries. And that is where simulations can help bridge the gap. But first, there’s a list of things that must come to fruition.
Machines are now able to build knowledge, continuously learn, understand language and interact with agents better than traditional systems.
Agents are expected to interact with machines to make faster information-driven decisions and help exploit information more effectively than commanders could on their own and develop awareness of these technologies, evaluating opportunities to pilot test, and demonstrating options for creating operational value.
48. How do Blockchain tasks show real-time agent readiness behaviour for distributed sequence cues formation of interactions in mission/function constraint space?
We present the following framework for the representational system of a distributed artificial intelligence task for solving constraint problems by individual agents. This framework serves as a guide for our product demonstration report. In the process of addressing its requirements, project developed some new concepts that hold promise for broader application to distributed constraint optimisation.
Agents sponsor product support activities case study to delineate any limitations, constraints or boundary conditions so obstacles to executing coordinated field-level operations are reflected.
Much of what needs to happen is in areas of applications and bandwidth, basically getting better versions of terrains and simulations that are more realistic and can accommodate as much as a division’s worth of players and an equally complex, simulated adversary.
49. What are examples of trade-offs between industrial requirements and multi-agent systems?
Responsible Agents for Product-Process Integrated Design Project is developing agent-based tools using market place signals among members of a distributed design team to coordinate set-based design of discrete manufactured products.
Trade offs between industrial requirements and Multi-Agent System characterisation in design, implementation, and testing are described. Like any industrial project, it begins with requirements of the problem domain, and draws selectively from results of investigations to meet those requirements.
Distributed problem solving artificial intelligence facilitate agent cooperation work where distribution of capability, information, and expertise make no single agent solution to tasks possible.
50. How do specialised agents formulate and combine Block component solutions?
In concurrent engineering, a problem could involve designing and manufacturing a vehicle by allowing specialised agents to individually formulate components and processes, and combining these into a collective solution.
Goal of distributed artificial intelligence is to develop mechanisms and methods that enable agents to interact better than workers and to understand interaction among agents. Key pattern of interaction in multi-agent systems is goal- and task-oriented coordination, both in cooperative and in competitive situations.
In the case of cooperation several agents try to combine their efforts to accomplish as a group what the individuals cannot, and in the case of competition several agents try to get what only some of them can have.
Planners want to concurrently speed-up problem solving by agents to determine what degree problem is characterised by parallel mechanisms.
Problem is to find sequence of moves with capacity to achieve the goal state. Another motivation for distributed problem solving and planning requires distributed agent expertise or other problem-solving capabilities
1. How will Digital Twins be used for faster decision-making and training?
As the Navy inserts more weapons and tools onto ships and aircraft, many needs have emerged for AI tools, especially given the service’s reliance on large data sets such as surveillance video and radar and sonar pictures.
“We tend to focus a lot on decision aids and how to use it to aid in decision-making speed. We look at it a lot of times on how to make sense of lots of data, whether it’s video processing or sonar processing or something along those lines.
An area we don’t talk enough about where there’s also very interesting opportunities is in training, and how do we speed up our training cycles. So if we get our acquisition cycle going very quickly, whether it’s building ships faster or putting new capabilities on ships, eventually we will be limited in its effectiveness by how fast we can train the crew and make them proficient.
Some of the use of AI is almost a training aid, that’s a whole other area of very interesting possibilities for us to deal with this kind of ever-changing world.”
2. What is an example of Digital Twin Application in the Fleet?
One early effort to create this kind of AI learning environment is through the use of the Aegis Digital Twin effort, which allows computers and consoles with the Aegis Combat System to be brought onboard a ship at sea and tap into actual radar and information feeds on the ship without interfering with the crew’s ability to navigate and fight the ship.
The consoles running Aegis with real-time data from the ship could be used to test out new capabilities that the Navy hopes to field and wants to solicit user feedback first, or it could be used for realistic training aboard the ship.
The Navy has said that adding artificial intelligence to this existing Aegis Digital Twin setup would allow the system to understand each user and track past performance, creating future scenarios based on proven weaknesses or areas that haven’t been tested previously rather than providing cookie-cutter training to all sailors.
Leaders are also eager to incorporate AI. Navy needs to buy ships and planes, but if it can’t also field enablers then it won’t win a high-end fight. One of the biggest enablers is digital supremacy. Who can turn knowledge into decisions most quickly.
In future combat, we need systems to get to the right decision most quickly. And what enables decisions? Turning a lot of data over to algorithms to crunch it to help give a decision – what most people call artificial intelligence.
3. What AI actions must be taken to improve the way Navy operates
Concept of turning data into the right decision more quickly – or more importantly, pointing out when something is going south – is incredibly important. So artificial intelligence … will help speed decisions.
Advances in information as warfare would improve “the ability to command and control a fight, the ability to sense an environment. ability to direct fires, integrate fires. That’s information as warfare, and that’s what we’re working on.”
But the Navy can’t just incorporate new technology for the sake of modernising; it needs to fully rethink how it conducts missions and how it buys new technologies, in the wake of this new digital warfare world.
We need ability to go faster, being able to think about things differently, how we approach things, how we buy things, how we require things and how we get after the fight.
“We can keep improving the way we’re doing business today, but if we need to do business completely differently in order to move at speed and scale, then we’ve got to figure out what that different is.
4. How is AI being used in ongoing wargames?
Navy is expanding its attack submarine war game strategy to further emphasise enhanced “spy” missions like intelligence, surveillance reconnaissance missions to quietly patrol shallow waters near coastline - scanning for adversary enemy submarines, surface ships and coastal threats.
Improved undersea navigation and detection technology, using new sonar, increased computer automation and artificial intelligence, enable quieter, faster movements in littoral waters where enemy mines, small boats and other threatening assets often operate.
Virginia-Class submarines are engineered with a “Fly-by-Wire” capability which allows the ship to quietly linger in shallow waters without having to surface or have each small move controlled by a human operator.
With “Fly-by-Wire” technology, a human operator will order depth and speed, allowing AI tools to direct the movement of the planes and rudder to maintain course and depth.
The ships can be driven primarily through AI code and electronics, thus freeing up time and energy for an operator who does not need to manually control each small manoeuvre. Previous Los Angeles-Class submarines rely upon manual, hydraulic controls.
This technology, using upgradable and fast-growing AI applications, widens the mission envelope for the attack submarines by vastly expanding their ISR potential. Using real-time analytics and an instant ability to draw upon an organize vast data-bases of information and sensor input, computer algorithms can now perform a range of procedural functions historically performed by humans to increase speed of manoeuvre and an attack submarine's ability to quickly shift course, change speed or alter depth positioning when faced with attacks.
5. What is a good example of where digital twins are being used at the operations level?
A good example of where digital twins are being used at the organisational level is in military manoeuvres. By creating a digital twin of the mission space, Marines can get powerful, real-time insight into combat workflows.
Using sensors to monitor troops and coordinate equipment, digital twins offer a better way of analyzing processes and alerting commanders at the right time when immediate action is needed. As a result, operational wait times can be reduced and workflow can be improved, decreasing operational costs and enhancing mission outcomes.
6. Variation exists is in how digital twins are implemented?
Digital twin technology can be used in new and mature ways, integrating sophisticated sensors, AI, and machine learning, to solve the biggest organisational challenges. In order to maximise their usefulness, digital twins need to be powered by high-performing databases that can pull together and process many data sets in real-time.
Where digital twins offer new and remarkable possibilities is at the organisational level in the built environment. Implementing digital twins offers the potential to create beneficial outcomes not only for administrators but also for the manoeuvre troops. In this way, digital twins can be used to take a troop-centric approach and then look at problems and context, and finally adding network systems and connected devices to try to solve big problems and create long-term value.
For organisations that already use advanced networks, digital twins are the next step along the digital journey. Digital twins can be used to improve efficiencies, optimise processes, detect problems before they occur, and innovate for the future. If your organisation is interested in producing better operational outcomes digital twins must be explored
7. How do Agent based systems contribute to operational areas like logistics?
Integration of the Simulation exercise that we have carried out at in different logistics scenarios validated that the agent-based technology is viable in situations where the maintenance planning problem is constantly changing e.g. project driven production, and requires frequent and continuous re-planning.
In these Readiness Simulations the collective aspects of the agent technology have been exploited. At the same time we have identified a great potential of the technology in situations where the planning problem is characterised by complex processes but it features some of the internal logistics.
In the case of the high volume production/maintenance availabilities, where not only collective aspects of agent simulation technology have been used but also the integrative capabilities of agents have been exploited e.g. integration of the linear programming heavy-duty solver.
In simulations where the planning metrics are widely distributed and not fully available the agent technologies provide an robust integrative and distributed planning framework for supply chain administration and virtual production/maintenance organisations formation.
8. What are the prospects for new digital enterprises?
Integration of virtual enterprises must be developed and their use must be populated through examples and application experiences. The objective is to develop and validate a step forward in the state of the art of Digital Architectures for Enterprise Integration.
Objective consists of architecture section selection to describe and present all the necessary activities to establish, carry out and complete an enterprise integration programme for any kind of enterprise.
In the case that an enterprise fails to perform its duties, the Virtual Enterprise must be reconfigured to replace the failing enterprise with another one. To support this functionality it is nice to have a distributed digital process plan/model tool to allow for re-planning and re-scheduling of logistics processes.
To support function of Virtual Enterprise-- independent of Virtual Enterprise size there is a need for a Virtual Enterprise coordinator. to monitor distributed logistics process Job Status and comparing it to Virtual Enterprise plans as described in the contracts.
9. What requirements and components must be put together by digital standards teams?
Any kind of proposal for an enterprise integration reference architecture can be evaluated under certification criteria.
Strong reliance on standards will contribute to facilitate the interfacing of existing applications with the virtual infrastructure, but unfortunately not all classes of information that need to be exchanged among virtual agents are covered by existing standards.
Initiatives of application agent groups contribute to facilitate this process. In general, it is necessary to develop some interface/mapping layer, at each enterprise, to adequately have this enterprise interacting with virtual infrastructure.
10. How does Blockchain Tech Utilise Cooperative Agent Structures?
Here we describe single Phases of cooperation life-cycle on Enterprise-to-Enterprise level searching of the possible product support collaborators. First, agents have to contact possible partners. There is wide field for future research in the domain of automatic search cooperation and contacting possible partners.
This Blockchain approach ensures the trustworthiness of the partners transferred from real-life to the agents cooperation. Each agent is equipped by the addresses and the security certificates and every partner can be authenticated using standard key methods. Every agent can be connected to many partner agents according to defined internal cooperation rules.
Once the agents are connected together, each agent provides the list of available product support capabilities to partners. It is possible to propose different capabilities to different partners. During this phase agents form basic cooperation network, receiving information suitable for effective collaboration in the next phases. During the life-cycle of the cooperation, agents subscribe information of the changes on product support resources on already established platforms.
11. How to balance transition from produce many parts from a few orders to producing a few parts from many orders?
“It’s definitely one of the biggest roadblocks right now. “Even if you’re fast, it usually takes significant block of time to prepare a quotation for an order. There’s also a lot of ‘communication “ping-pong” at the beginning: someone sends a request, you check if the part is printable, it’s not, so you request changes back and forth, and it just goes on like that.”
Fortunately, you can avoid all that communication “ping-pong” by automating your quotations and printability checks. Our Enterprise Platform is designed to do just that. In addition to giving you direct access to our network of print parts suppliers, the platform can also evaluate models for pricing and printability, include side-by-side comparisons of internal 3D printers with those in your on-demand prototypes network
12. How do AI tools provide complete visibility on manufacturing/service operations,?
Multi agent systems can be used also for a digital simulation and modeling of the production process or the supply chain, where they easily simulate an independence of involved parts. These tools can help to answer non-trivial tasks – how changes in single component will affect the production process or supply chain as a whole.
AI applications also help us improve the accuracy of our work orders; engage in more efficient production scheduling; enable interaction with our diverse vendor supply chain and reduce logistics delays for parts.
We have also noticed most legacy applications were designed for a enterprise-centered local operation and to be operated by agents. In order to have these applications supplying information to or consuming information from the virtual network, it is clearly necessary to extend their functionality.
13. Why is Interoperability among enterprise applications such a big challenge for supporting rapid formation of virtual scenarios?
in response to new operational opportunities it’s important to consider that each enterprise may have its own way of doing business. The level of information sharing among virtual agents is likely to either change of the trust level among partners or with adjustments to configuration in time.
So flexible virtual coordination and information visibility rights definition mechanisms are required to support both the autonomy and change order properties in behaviour of virtual agents.
Multi-agent solutions exist for low-level scheduling or control systems as well as product-configuration and quote phases to be used for short- and long-term production planning and supply chain administration.
14. What are the determinants of agent behaviour for Enterprise levels?
Multi-agent systems on intra-enterprise level and extra-enterprise level are independent in the digital population point of view. Agents used on intra-enterprise level are operating inside an enterprise represents many units or processes in the unit. On extra-enterprise level, whole unit is represented by a single agent, providing all abilities and services, available in the company.
If agents on both levels are used, a special Digital Twin agent can exists that bind both levels together. For digital application purposes, both levels can be modeled together as a Digital Twin to study and improve their abilities.
15. How should field-level job sites incorporate AI?
Advances in artificial intelligence have given rise to behavioural technologies capable of performing tasks that traditionally required intelligence of commanders in the field. Realising end-to-end digital enterprises, automating tasks and processes, and making smarter, faster decisions all require next frontier of technologies to transform the way agents and machines interact.
Must support realistic virtual Job Sites to test agent behaviours. The architecture should support construction of very responsive physical and behavioural mission space and stand in for the challenging real world counterparts in which agents are designed to function. This is a particular strength of an architecture that incorporates state-of-the-art simulation capabilities.
16. How will advances in AI shed light on how deployment decisions will affect forces in the long run?
By analyzing historical trends along with real-time data, AI tools could show how a unit’s readiness would change if it were, for instance, moved to a new location or given additional resources
Marines are also building a separate AI system that ranks course of action plans based on those extrapolations, which could one day be merged with the readiness system.
“You integrate that all together and you get a full view of readiness across your force. Now you can really make some data-driven decisions.”
The next stage of the effort will include parts of the Marines’ aviation and logistics units, bringing about half branch into the purview of the program. With that additional data, the AI would further refine its processing rules to deliver better results.
So artificial intelligence is tasked with managing the particular deployments of troops in battle, moving them around in new and unexpected ways.
17. What are some practical applications of AI that could be fielded soon?
One way that future developments in AI might manifest is by looking at a place where AI already manages workforce inventory-- like a warehouse stocking system, a process in which items are unloaded wherever there is space in a warehouse and then scanned into a computer system than can track where the item is located.
When it comes time to retrieve an item for delivery, the same computer system directs warehouse workers to the most efficient route for finding the item, which could be stowed throughout the warehouse.
When modeling the warehouse system, it is interesting to consider how AI, given the same objectives as a commander, might organise and direct forces to achieve them.
“Why would an AI allocate forces in distinct areas of the battlefield? It could intermingle them and manage them at a granular level. Its categories are way more numerous, in the way that warehouse AI manages categories at the shelf level.
Instead of distinct groupings of armour, air support, infantry, and artillery, a system run by artificial intelligence and managing a battle could coordinate a single helicopter with a pair of howitzers and an infantry platoon, directly grouping each in the same way that a warehouse worker finds an assortment of items to place into the same package.
18. How do Multi-Agent Models contribute to AI development?
One of the main underlying trends in Training development is the focus on agent models, protocols, and mechanisms to support the collaboration of pre-existing entities in distributed real-world scenarios, be it among organisations, among Troops, or among Troops and organisations.
It’s important to continually assess approaches to systems training/engineering, including use of agent models, simulation results, artificial intelligence techniques, and tools supporting training process so it is possible to stay ahead of demands for new and upgraded weapons systems.
Using agent models is not a new concept; however, digital engineering will address long-standing challenges associated with complexity, uncertainty, and rapid change in deploying defense systems. By providing for more agile and responsive development, digital training/engineering supports engineering excellence and provides a foundation for mission success in the future.
Realising end-to-end digital enterprises, automating tasks and processes, and making smarter, faster decisions all require next frontier of technologies to transform the way agents and machines interact. Advances in artificial intelligence have given rise to behavioural technologies capable of performing tasks that traditionally required intelligence of commanders in the field.
19. What multi-agent technologies could speed AI development?
Covering all requirements of modern military logistics enterprise simulations is not easy problem. We propose two agent-based technologies for logistics support on two different levels: intra-enterprise and extra-enterprise level-- can be used simultaneously or together.
The standard configuration consists of several independent systems linked to the virtual organisation by agent-based decision support technology in various fields of logistics – maintenance planning, supply chain decision-making models, simulation, extra-enterprise access, etc.
Although established architectures have many good points, all these architectures can be improved, since they have not completely generated the necessary digital modeling techniques and adequate execution tools for the different kinds of enterprise architectures and specialised tools focus on necessities of every type of enterprise activity.
20. How does AI contribute to digital engineering tech like generative design?
Generative Design is new approach to engineering set to transform the potential of manufacturing over the next decade. It’s a process that enables engineers using digital tools to define an engineering problem, and solve it over and over again by an adaptive artificial intelligence program, yielding different results each time.
if you pool enough computing horsepower, your network is capable of completing many iterations of very complex tasks very quickly. Questions about the potential for many designs used to be fairly straightforward. Design teams have a finite amount of time and money at their disposal, and can’t afford to prototype more than a few designs—let alone thousands.
But what are these constraints costing you, in terms of the ideas that never get tested and the solutions that never get prototyped? Are there unexplored methods of building your product that might be lighter, faster or cheaper?
21. Why is the power of parallel computing going to help us test more ideas and look at more concepts in a shorter period of time?
The “secret sauce” is an AI platform that has been “trained” to create solutions to engineering problems. Unlike human engineers—who first design a solution, then determine how to build it, then prototype and test its properties—generative design is capable of carrying out all three of those steps simultaneously.
The process requires human engineers to define the problem by using established systems to lay out basic specifications for the component that needs to be designed. “You don’t start with the geometry. You start where it attaches to other components and how.”
After that, engineers further refine the design parameters, specifying load requirements, deflection, rigidity, material preferences, cost of production, weight requirements and even manufacturing methods, attention to detail is critical during the process of defining problem.
“If you input junk you’re going to get back junk, essentially. “But if you actually specify the requirements early and set up to solve the right problems, that’s when you’ll see the benefits of the tools.
22. How is Marine Corps is experimenting with AI to improve the way it deploys its forces?
The Marines built a tool that crunches data on personnel and equipment to measure how prepared individual battalions are for combat and spot potential weaknesses years in advance.
Today planners rely on spreadsheets, whiteboards and basic applications to track readiness and manage forces, but artificial intelligence can offer them a better understanding of the resources at their disposal and the long-term effects of the decisions they make.
The tech crunches both structured and unstructured data from multiple force management applications to create a real-time image of how prepared each unit is for combat. The tool specifically aims to build a five-year management plan for the Marine infantry battalions.
Allocating the service’s resources is an imperfect science. Leaders map out deployment strategies years or even decades in advance, but situations will invariably arise that throw a wrench in those plans.
Planners are constantly forced to “reshuffle the deck” as crises flare up in different places and figuring out which units to move around is a complicated process. Numerous factors—training, deployment history, equipment readiness and others—affect how prepared a group is for a given situation.
23. What is the strategy AI tools use to impact battlefield planning?
Tool has two primary functions: It flags the units that are most ready for action and explains why others come up short. Armed with that knowledge, commanders can proactively train and invest in less prepared groups before they fall even further behind.
“A lot of times Mairnes only invest more when the problem arises. Now they can see it ahead of time and say ‘OK, we’re going to take action now to prevent that from occurring.’”
The tool sheds light on how deployment decisions will affect forces in the long run. By analyzing historical trends along with real-time data, the tool could show how a unit’s readiness would change if it were, for instance, moved to a new location or given additional resources.
Marines are also building a separate AI system that ranks course of action plans based on those extrapolations, which could one day be merged with the readiness system.
“You integrate that all together and you get a full view of readiness across your force. Now you can really make some data-driven decisions.”
The next stage of the effort will include parts of the Marines’ aviation and logistics units, bringing about half branch into the purview of the program. With that additional data, the AI would further refine its processing rules to deliver better results.
24. How does AI manages field-level battlespace workforce inventory?
Instead of distinct groupings of armor, air support, infantry, and artillery, a system run by artificial intelligence and managing a battle could coordinate a single helicopter with a pair of howitzers and an infantry platoon, directly grouping each in the same way that a warehouse worker finds an assortment of items to place into the same package.
An example of AI application is a warehouse stocking system, a process where items are unloaded wherever there is space in a warehouse and then scanned into a computer system than can track where the item is located.
When it comes time to retrieve an item for delivery, the same computer system directs warehouse workers to the most efficient route for finding the item, which could be stowed throughout the warehouse.
When modeling the warehouse system, it is interesting to consider how AI, given the same objectives as a commander, might organise and direct forces to achieve them.
“Why would an AI allocate forces in distinct areas of the battlefield? It could intermingle them and manage them at a granular level. Its categories are way more numerous, in the way that a warehouse AI manages categories at the shelf level.
25. How is artificial intelligence is tasked with managing troop deployments to move around in new and unexpected ways?
“Anytime we’re on the road, our job, maintenance wise, is to provide safe and reliable jets for the pilots to accomplish their mission. Every new location presents a different challenge in how we get the job done, but the end goal for providing a safe jet for a pilot never changes. What does change is the environment in which we operate in.”
“Every exercise you go on is different, and it can be hard to start off. It could be not having the parts we need on hand, or not knowing how the base operates to get the support we need. Over time you figure out how to acquire some of that on site, what to bring along yourself and how to solve a problem before it becomes one.”
Here we consider how AI systems could be useful to a typical work order job of launch and recovery of aircraft, engine maintenance and servicing of life-saving equipment-- just a few of dozens of tasks Troops are expected to accomplish within a full day.
26. What obstacles does the AI sector face to gain traction with military?
Even while the groups and organisations hyping artificial intelligence solutions popped up everywhere at the expos with promises to create the next battlefield advantage using next generation weapons, gear, or satellites. The term artificial intelligence splashes the headlines with promises that we’re moments away from revolutionising the battlefield.
It’s frustrating. The special AI “task forces” and their massive budgets are great, but it’s time to get honest about the rest of the military. Ask any Marine their opinion of how things run on a daily basis and you will hear complaints about lost orders, broken gear, and outdated technology.
Bottom line: all those flashy AI applications being touted as perfect for Marines use are not going to run on the outdated infrastructure on which a majority of the military still operates.
That doesn’t mean that AI isn’t a good fit or shouldn’t be pursued. But it does mean that AI success requires a force readiness approach. First, AI isn’t new and it isn’t new to the military.
Marketing hype around the term has experienced a surge lately but the fact that something wasn’t tagged as artificial intelligence historically does not take away the fact that it was actually AI.
Despite the hype, AI is simply a field of science that trains systems to perform some human task through learning and automation. There are varying degrees of sophistication but most of the mining, network assessment constructs and mapping technology used over the past decade or more have all been forms of AI.
Weapons systems and combat vehicles have been leveraging AI for many years as well. So don’t let the noise change the focus from the mission need.
There are varying degrees of complexity but most of the data mining, network engineering and mapping technology used over the past decade or more have all been forms of AI. Weapons systems and combat vehicles have been leveraging AI for many years as well.
27. What developments need to happen to improve AI collection of data?
Marines on the front lines need their supporting forces to be trained and armed with the appropriate technology to support the advances being operationalised on the battlefield. If we look specifically at the intelligence arena, the vast majority of military intelligence analysts are still using the same products and systems from 10-15 years ago.
Efforts around collecting intelligence are ripe for avances, but what about the Marines that have to sift through and make sense of that additional data? How has their training changed to account for a more technologically advanced battlespace? How do products and solutions integrated requirements and workflows with real time information truly augment their efforts?
The majority of data mining and visualisation tools on the market have flashier interfaces than we saw a decade ago, but the true sophistication of what the vast majority of Marines have been offered doesn’t really reflect the decade of advancements seen in the commercial market.
28. What are some examples of operational value provided by AI?
You don’t have to be a part of a high profile AI initiative to find value in the technology for nearly all areas of the military. We need the whole force to have the technical advantage on the battlefield and that means AI must become a force readiness initiative.
It’s all about augmenting human efforts across battalions, regiments and divisions to raise the readiness levels of the entire force. Marines inside the wire should have the knowledge, technical skills and agility to support all of the operations and technology our troops outside the wire are running.
Then there’s the applicability across all military systems. An “AI watchman” could prevent ships from colliding with one another since the computers are “constantly looking at sensor data and is making sense of the environment and the situation.”
“There is that safety aspect of using artificial intelligence to augment the level of capability and intelligence available on ships, on tanks, in aircraft, all over, where you almost have an embedded AI technician be part of every military asset.
29. What are some obstacles to AI use of data?
Programs find themselves overwhelmed by the sheer volume of data, putting the program at risk, if data spends too long in the review state, missing the contractual response deadline.
Without a vector check from DoD, the contractor assumes a conditional acceptance and moves forward, assuming they are on the right track. This could lead to a host of problems, including a possible schedule slip.”
The team is now spreading the proven results of tools and showing individual operational units can use the tools to suit various purposes. Versatility is inherent in the review and content-sharing applications, so new users usually recognize ways to streamline the bulk of documents that shuttle through an acquisition enterprise.
The objective is simple: information dominance through the creation, review, approval and dissemination of data. If you have unrefined, makeshift processes, this won’t work for you, at least not yet. Business processes work more efficient with automation so there are now options if your workflow is functional, but uses outdated tools or requires intense use of workforce capital.
30. How can Blockchain contribute to field-level work order accuracy?
Every agent in the “Blockchain” can see, through comment tracking, how data changed to capture the correct info and meet requirements.
“The advantage of the utilities lies in the ability to collaborate on existing work, review past work and evolve the system architecture to meet changing needs. These tools provide value now, and value in the future by giving agents access to program work performed
This fidelity, coupled with the capacity to short-circuit future process complications, makes work order content-sharing applications ideal for long-running, complex weapons systems sustainment program offices looking to limit volume of work spent on acquisition schedule.
Blockchain bucket-brigade content for sustainment execution, operational manoeuvre etc. communication interface is evaluated in the implementation of space-time parallel applications for massively parallel machines.
The simplified version of in-time contract content work order rules is valid in time-dependent problems, and that it can be implemented as the form of the bucket-brigade communication with simple computations.
31. What are some effective strategies for moving toward large scale computing?
Must evaluate performance of several configurations by the use of interface for the bucket-brigade communications. On the basis of robot/agent for sustainment business operation performance measurements or any other practical applications,.
There is way to parallelise operations dynamically on a “Blockchain” while maintaining both decentralisation and security. Looking on the internet, no one spelled out this design in particular. It might actually help with scaling.
In contrast to just breaking up the whole system into smaller parts like you do in a bucket brigade, this design provides a much higher adaptability to the current utilisation of the communication network, however, does not improve the storage consumption on each node like breaking up into small parts does.
There’s no reason not to implement this strategy on top of current practise for increasing flexibility and weapons system sustainment work order transaction throughput. Another factor to consider is that many of the top “Blockchain” projects are still utilising proof of work.
32. Why is it difficult to change agent consensus rules process?
Changing consensus rules requires some time. Programs find themselves overwhelmed by the sheer volume of data, putting the program at risk, if data spends too long in the review state, missing the contractual response deadline.
Without a vector check from DoD, the contractor assumes a conditional acceptance and moves forward, assuming they are on the right track. This could lead to a host of problems, including a possible schedule slip.”
The team is now spreading the proven results of tools and showing individual operational units can use the tools to suit various purposes. Versatility is inherent in the review and content-sharing applications, so new users usually recognize ways to streamline the bulk of documents that shuttle through an acquisition enterprise.
The objective is simple: information dominance through the creation, review, approval and dissemination of data. If you have unrefined, makeshift processes, this won’t work for you, at least not yet.
Business processes work more efficient with automation so there are now options if your workflow is functional, but uses outdated tools or requires intense use of workforce capital.
33. How do agents work together to execute Blockchain contracts?
An agent can make mutually exclusive low-commitment offers to multiple agents. In the case more than one accepts, the agent can backtrack from all but one so agent can address the other parties in parallel instead of addressing them one at a time and blocking to wait for an answer before addressing the next.
For example, if an agent wants one particular contract, it can offer that contract to several parties with meaningful commitment instead of no commitment at all that would be strategically meaningless. Load balancing is crucial for parallel applications since it is representative of good use of the capacity of the parallel processing units.
Here we look at applications putting high demand on the parallel interconnect in terms of throughput. Examples of such applications are compression applications which both process important amounts of data and require a lot of computations undesirable for most parallel architectures. The problem is exacerbated when working with heterogenous parallel hardware. This is the case when using a heterogenous cluster to execute parallel application
Reinforcement consists of redistributing bids made between subsequently chosen rules. The bid of each winner at each time-step is placed in a "bucket". A record is kept of the winners on the previous time step and they each receive an equal share of the contents of the current bucket; fitness is shared amongst activated rules. If a reward is received from the work space communications then this is paid to the winning rule which produced the last output. Each rule is much like the middleman in Bucket Brigade Blockchain.
34. What are some anticipated difficulties to wide-scale Blockchain application?
As we have outlined in this report, Blockchain is an emerging technology for decentralised and transactional supply line connection monitor sharing across a large group of supplier Network intersections. It enables new forms of distributed supply line connection monitor networks, where agreement on shared states can be established without trusting a central integration point.
A major difficulty for architects designing applications based on blockchain is that the technology has many configurations and variants. Since blockchains are at an early stage, there are limited number of product support success stories or reliable technology evaluation available to compare different blockchains.
35. What improvements does Blockchain bring to supply chain management for manufacturing and parts suppliers?
Blockchain is the digital and decentralised exchange of value technology that records all transactions without the need for an intermediary. Businesses aim to manufacture goods — whether end products, solutions or precision parts of the highest quality, for the best price, with the greatest technical support, and according to agreed timelines.
Blockchains enable the creation of intelligent, embedded and trusted programme supply line connection monitor, letting suppliers build terms, conditions and other logistics parameters into contracts and other transactions. It allows suppliers to automatically monitor agreed upon value figures, delivery times and other enabling conditions, and automatically negotiate and complete transactions in real time. This impacts cost/benefit of work orders, maximises efficiency and allows for multiple avenues leading to supply line connection monitor.
36. Why is it important for Blockchain systems to store work orders?
A blockchain is a shared, distributed, secure supply line network connection monitor that every participant on product support service routes can share, but that no one entity control. In other words, a blockchain is a supply line connection monitor that stores work order routing records. The routing intersection is shared by group of service route supplier participants, all of whom can submit new records for inclusion.
Blockchain records are only added to the supply line connection monitor based on the agreement, or consensus, of a majority of the supplier group. Additionally, once the records are entered, they can never be changed or erased. In sum, blockchains record and secure supply line route dispatch information in such a way that is becomes the agreed-upon record for stakeholders of important contract terms and enabling conditions.
Smart contracts can be instantly/securely sent and received over the Blockchain Network reducing exposure/delays in back office dispatching. As an example, oversight of Purchase Requests could be securely implemented with greater transparency and also potential battlefield applications messaging system could be leveraged during instances in which troops are attempt to communicate back to HQ using secure, efficient and timely logistics system.
Built-in supplier incentives to assure the security of every transaction and asset in the blockchain allows routing technology at intersections to be used not only for transactions, but as a product registry system for recording, tracking and monitoring all assets across multiple value suppliers. This secure information can range from information about parts or contract work-in-progress such as product specifications and purchase orders.
37. Why are Blockchain connections more reliable than traditional technologies?
Because blockchain is based on shared consensus among different suppliers, the information on the blockchain is reliable. Over time, suppliers build up a reputation on the blockchain which demonstrates their credibility to one another. Furthermore, because trust can be established by the supply line connections, third party monitor of routing intersections between two suppliers will no longer be necessary.
In order to establish sufficient trust to become involved in a blockchain supply line connection monitor, the motives and goals of DoD and involved suppliers must be clear. The reputation of the participants becomes transparent and grows over time. It is important that suppliers in the routing market space can trust each other in order to share information and increase efficiency in shared processes.
Blockchain networks also open the door for machine-to-machine transaction capabilities to enable the transformation of a traditional supply line connection, where work order transactions and contracts must be maintained by each DoD dispatcher agent in market interaction with suppliers.
38. How do agents work together to solve distributed problems?
Distributed problem solving concentrates on competence; as anyone who has played on a team, worked on a group project, or performed on a football team can tell you, simply having the desire to work together by no means ensures a competent collective outcome.
We have described Single Phase of cooperation life-cycle on Enterprise-to-Enterprise level search for possible product support collaborators. First, agents have to contact possible partners. There is wide field for future research in the domain of automatic searching and contacting possible partners.
This approach ensures the trustworthiness of the partners transferred from real-life to the agents cooperation. Each agent is equipped by the addresses and the security certificates and every partner can be authenticated using standard key methods. Every agent can be connected to many partner agents according to defined internal cooperation rules.
Once the agents are connected together, each agent provides the list of available product support capabilities to partners. It is possible to propose different capabilities to different partners. During this phase agents form basic cooperation network, receiving information suitable for effective collaboration in the next phases. During the life-cycle of the cooperation, agents subscribe information of the changes on product support resources on already established cooperation.
39. What network Marketing problems can be fixed with field-agent mechanisms?
Establishing field agents for product/process design creates agent-based tools to construct market places among members of a Distributed design team to coordinate set-based design of a discrete build product. Designers of components are empowered to "Buy" and "Sell" desired characteristics engineers are motivated to assume.
Here we describe the entities interacting in the market space and outline the market space required to make trade-off decisions on each characteristic of a design. Agents representing each component "Buy" and "Sell" units of these characteristics. A component that needs more latitude in a given characteristic, i.e. more weight can purchase increments of that characteristic from another component, but may need to sell another characteristic to raise resources for this purchase.
40. Why Is the position/order tracking tag so important in Blockchain Systems?
In network marketing, each participant essentially has one core asset- the position/order tracking tag in the network recorded with fidelity. Tokens “locked” into the system provide privileges in using the system as intended. It could be revenue share in a typical marketing scheme or it could be a discount on risk charged by the system charges for its services.
In Distributed problem solving, we typically assume a fair degree of fidelity is present: the agents have been designed to work together; or the payoffs to self-interested agents are only accrued through collective efforts; or engineering relationships between units has introduced disincentives for agent individualism; etc.
41. What needs to happen with Blockchain to enable advances in Digital Twin application?
Most block chain service providers offer only narrow class of use cases. Site Visit Executive looked at the problem and collapses the apparent wide-range of Blockchain services into couple of topics: supply line optimisation and streamlining network marketing processes.
Before DoD has Blockchain in some form, no new “Digital Twin” model can work. Spatial navigation became possible after DoD got GPS sensors in its pockets; Situational awareness became possible after DoD got the sensors connected.
Not side products but instead secondary products of Blockchains have broadly adopted tokens and develop into applicable results such as “Bucket Brigades”.
Bucket brigade systems are a tool to build robust simulated robot control systems. This choice is sufficient to achieve adequate levels of performance for a variety of behaviours. The parallel implementation of the bucket brigade system is sure to speed up the training process and implement robotics controller.
42. How do Blockchain Systems contribute to task learning?
Bucket brigade systems provide guidance shortening the number of cycles required to learn task rules using only a few training examples starting with randomly generated classifiers. Bucket brigades compensate for lack of time to deploy field-ready tech.
Most DoD Leaders associate bucket brigades with disasters, but they are mostly used in normal conditions to load or unload something, for example a truck of ammo supplies. Another example of Bucket Brigades is warning beacons,
In every case, Bucket Brigades compensate for the lack of technology, temporary or permanent. Information transfer is compromised by technological shortcomings of all networks not able to allow for multiple interpretations or deliver the information with fidelity.
43. How does multi-agent approach provides a specific alternative to known system model tech for simulating manoeuvre process?
We set up experiment involving “Digital Twin” robots learning to work together: one robot ideally handing off Tokens to the other, which in turn carries them to a final destination. Discounting rewards results in the first robot receiving significantly less reward than the second one.
Tokens are transferred between Digital Twin agent pairs in the Bucket Brigade, without any centralised system watching or authorising interactions.
In a tech set without Bucket Brigades, now a stronger than ever trend to automate with machine learning, each node is as stupid as a box of rocks, unless it is perfectly prepared for the job. Each element has to be precisely calibrated or the system is in major trouble. Even if some aperture is allowed, an error propagation is a strong and adverse phenomenon.
44. Why is it necessary for Blockchain to collect info on the frequency of reward?
Blockchain solutions have information about how many times a resource was used as reward and for what segment of equipment, so you can see in what operational theatre the discount effect is greatest. Robots can write and pass comments on it.
Tokens aren’t anything new. Each of us use incumbent tokens daily without even noticing: keys, tickets, receipts, reservations, network certificates etc. Ordinary tokens are already vital, but they are not very smart. More importantly, they are expensive to issue and even more expensive to maintain, with a large portion of barriers to implement system associated with security.
45. How can Blockchain change the way of doing business and consumption of operational resources?
As many Blockchain bridge connections are manifest in operations, tokens promise new productive economic scenarios when random “Digital Twin” token pairs can become mutually usable in changing conditions as a result of more reasonable inputs so many essential operational parameters can be articulated with greater precision.
On one hand, smart decision-making can be rewarded immediately in tokens. On the other hand, it always costs some number of some tokens to do something. So DoD operatives either make an economically-responsible decision or do not contaminate the feed, abstaining from any involvement.
As open token-carrying platforms become more widespread, anyone can maintain an unlimited number of token output. Soon, piles of tokens will represent everything that can be counted in an economically meaningful way.
Within token-enabled supply chains, every participant seamlessly contributes to the quality automated event flow. Most economic acts can be done through token exchange, issuance or redemption. When tokens circulate, things you normally run operations, and procurement on happen “by themselves”, with much less overhead than normal.
46. How do Blockchain transfer mechanism address changing scenarios?
When being passed continuously between units, tokens be very, very smart when it comes to changing scenarios.
Passing Tokens from one unit in the Bucket Brigade to another is a fundamentally local event. No one else but the units party to this exchange is required and if we consider distributed token-carrying platform connection bridges between them as free-access, self-maintaining ownerless entities.
Examples of that ubiquitous miracle of local interaction are everywhere: The great complexity of physical phenomena troops encounter is the result of endless iterations of similar “local acts”: circles on the water don’t need a concentric dispatcher.
Bucket Brigade Junctions in token interactions can be much smarter: since the constructs can also bear an often-needed note of context rather than the iron extremes present in automated operations.
But what about more complex things than just transfer of value, such as level of local interaction relevance under changing operational conditions where quality of information is not possible to quantify..
47. How can AI simulators enable commanders to lead virtual troops?
Some of the advanced weapons can’t be demonstrated where just anyone can see them in action, thus revealing our tech to adversaries. And that is where simulations can help bridge the gap. But first, there’s a list of things that must come to fruition.
Machines are now able to build knowledge, continuously learn, understand language and interact with agents better than traditional systems.
Agents are expected to interact with machines to make faster information-driven decisions and help exploit information more effectively than commanders could on their own and develop awareness of these technologies, evaluating opportunities to pilot test, and demonstrating options for creating operational value.
48. How do Blockchain tasks show real-time agent readiness behaviour for distributed sequence cues formation of interactions in mission/function constraint space?
We present the following framework for the representational system of a distributed artificial intelligence task for solving constraint problems by individual agents. This framework serves as a guide for our product demonstration report. In the process of addressing its requirements, project developed some new concepts that hold promise for broader application to distributed constraint optimisation.
Agents sponsor product support activities case study to delineate any limitations, constraints or boundary conditions so obstacles to executing coordinated field-level operations are reflected.
Much of what needs to happen is in areas of applications and bandwidth, basically getting better versions of terrains and simulations that are more realistic and can accommodate as much as a division’s worth of players and an equally complex, simulated adversary.
49. What are examples of trade-offs between industrial requirements and multi-agent systems?
Responsible Agents for Product-Process Integrated Design Project is developing agent-based tools using market place signals among members of a distributed design team to coordinate set-based design of discrete manufactured products.
Trade offs between industrial requirements and Multi-Agent System characterisation in design, implementation, and testing are described. Like any industrial project, it begins with requirements of the problem domain, and draws selectively from results of investigations to meet those requirements.
Distributed problem solving artificial intelligence facilitate agent cooperation work where distribution of capability, information, and expertise make no single agent solution to tasks possible.
50. How do specialised agents formulate and combine Block component solutions?
In concurrent engineering, a problem could involve designing and manufacturing a vehicle by allowing specialised agents to individually formulate components and processes, and combining these into a collective solution.
Goal of distributed artificial intelligence is to develop mechanisms and methods that enable agents to interact better than workers and to understand interaction among agents. Key pattern of interaction in multi-agent systems is goal- and task-oriented coordination, both in cooperative and in competitive situations.
In the case of cooperation several agents try to combine their efforts to accomplish as a group what the individuals cannot, and in the case of competition several agents try to get what only some of them can have.
Planners want to concurrently speed-up problem solving by agents to determine what degree problem is characterised by parallel mechanisms.
Problem is to find sequence of moves with capacity to achieve the goal state. Another motivation for distributed problem solving and planning requires distributed agent expertise or other problem-solving capabilities
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