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Top 10 Virtual Reality Training Simulation Application to Field Activity Scenario Prototypes

12/11/2018

The future of Marine aviation is complex: aircraft are growing more technologically advanced, pilots face a proliferation of high-end and low-end threats, military budgets are squeezed and demand for Navy forces around the globe is growing.

So how will Marine aviation training keep up? In part, with fielding of tech advanced simulators.
Joint Terminal Attack Controllers using the simulator can coordinate with pilots in the air to identify and mark targets for air strikes from the ground

In a feat that combined live training and simulator training, we conducted a live, virtual and constructive demo. We took equipment that’s already on the aircraft that broadcasts the aircraft’s altitude, airspeed, position in real time, and we put a transmitter or receiver unit on the top of the building.

We were able to tap into that feed, and what that did was it took that feed of an actual aircraft on the range, and we piped it into simulator, and it was accurately recreated in the virtual workspace.

The aircraft is actually flying on the range and is properly displayed in the simulator with very low to minimal latency in a real-time altitude, airspeed, and attitude. So what that provided for is a real-time control of that aircraft with the ability to see the aircraft as well as have the ability to achieve visual recognition.

Marines are able to look up and actually assess the attitude and profile of that aircraft and then provide the clearance to essentially employ munitions on the desired intended target.

Before we had the simulator, we were really slow in the first few days on the range because that’s the first time operators did it. But now getting some practice time in, you get better control and better performance on the range with the live assets, so it makes it more efficient. So the simulator is really useful, it’s invaluable as far as getting Marines ready to go.

It has been harder and harder to get fleet aircraft that can support training due to a high operational tempo and due to challenges in keeping the aircraft ready to fly. The more training Marines can get on the range, the better they are when they actually get to an actual aircraft.

“So they’re not stumbling on Day 1, they’re already semi-proficient or trying to get there, whereas in the past before they had this simulator you’re a mess your first several times, so it’s good training for you, but for the guys airborne, they’re holding for a half hour just to get a bomb off because the guy on the ground is learning what to do.

The simulator has created a dramatic improvement in the first pass drop and the communications on the radio and everything. Marines work everything out here, so by the time that they’re on the range it’s just the real-life stuff that hits you. … A lot more first-pass drops, which is the whole goal of close-air support.

Want Marines will eventually be able to do is put this into a guy in a aircraft simulator and they’ll be running this simulator, talking to the guys in this simulator, and doing all their controls to get their currency requirements to satisfy their trainin while taking their targeting cues from other Marines in their own simulator.

A next step towards achieving that vision of connecting multiple simulators spread across the battlespace is the integrated training facility to house, all under one roof, simulators for pretty much anything in the carrier strike group.

We’ll be able to integrate them all together. Eventually we will be able to pipe in feeds from live aircraft out on our range – that’s the live part, and then vice versa hopefully we can pipe what’s being seen in the simulators, or what’s being constructed in the simulators, out to the live aircraft as well.

What we want to be able to do in the future, and this facility is the first step, is machine-to-machine data gathering. And that will allow us to gather large amounts of data – so not just necessarily how they did on that event, in the actual actions they took on that event, but we can also gather historical data on the aircraft, its system, how well the systems have held up.

We can look at, automatically, machine-to-machine, look at the pilot to assess proficiency, and see how much flight time was received recently, helping us build that bigger picture so we can inform leadership with the best information we can give them.

Despite the focus on high-end warfare technologies, aviators could face, equally dangerous less expensive threats like shoulder-launched anti-air missiles so we invested in Surface-to-Air Missile simulators to help ensure that pilots cycling through training events are aware of the threats they face on the ground and are flying with tactics that would keep them safe.

Though the SAM simulators aren’t connected to the planes in the air – so the pilot didn’t know in real-time he had been “shot” at with the simulator – the simulator logs video of the encounter. That video is incorporated into the pilot’s debrief after a training event, with the instructors explaining to the pilot whether his flight profile would have kept him safe or put him at risk to ground threats.

This is how you prove to Marines, you’re reachable, you need to be careful and you need to know what you’re doing, get your tactics right. Everything that’s out there is beatable, you’ve just got to know what you’re doing, but you’ve got to get your tactics right.

Readiness Tool allows top brass to determine which battalions and gear are most prepared for battle.

Marine Corps is experimenting with artificial intelligence to improve the way it deploys its forces and spot potential weaknesses years in advance.

The Marines built a tool that crunches data on personnel and equipment to measure how prepared individual battalions are for combat. The tool could ultimately help top brass deploy some 186,000 active-duty Marines and countless pieces of military hardware.

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.

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.

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.

So artificial intelligence is tasked with managing the particular deployments of troops in battle, moving them around in new and unexpected ways.

One way that future might manifest is by looking at a place where AI already manages workforce inventory-- like 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.

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.

“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.

“We learn to operate in new environments, out here we’ve adapted our operations to give the best support possible. Maintenance is maintenance, our job never changes, but how we execute the mission does.”

“Our main mission is to enable successful sorties by generating aircraft parts, ultimately maintaining our full spectrum readiness. Our team encounters new repairs that force changes in direction and orders, but they all adapt and constantly find ways to make sure the job gets done.”

Maintaining the aging aircraft can be challenging as some parts are no longer commercially produced and the Fabrication Flight must collaborate and innovate to construct parts on their own.

“We all need each other in order to complete a task and make sure operations are done correctly. “Everything revolves in a circle – sheet metals technicians hand over parts to metals technicians who follow their technical order before sending to nondestructive inspection to make sure the piece is good for use on an aircraft.”

To display the teamwork necessary, the Troops walked us through the Fabrication Flight process.

Sheet metals technicians , kick off operations by receiving technical orders for aircraft repairs. Troops survey the technical order and pulls a thin, malleable sheet from their collection. The sheet is then cut to the specific measurements and handed off to a metals technician to be heat treated in a large oven.

"On our side we handle breaking the metal down and then crafting it to match the technical order for the specific part. When completed, the piece is hauled over to nondestructive inspection where tests are conducted to ensure the part is compositionally sound and aircraft ready.

"With the resources we have here, we are the final stop on a part's journey to an aircraft,” "If anything is wrong with the part, it's flagged and sent back to the workshop to either correct the issue, or start the operations all over again."

Accuracy in fabrication is essential in getting aircraft back up flying. When the part has completed all processes and is cleared for use, it is installed onto the aircraft, restoring readiness of the aircraft.

Fabrication flight Airmen gain a sense of accomplishment by witnessing their work come to fruition each time an aircraft takes off.

“Having combatant commands and other mission partners on base only adds to the importance of mission success. We take pride in the work of the flight, seeing the aircraft out there completing missions thanks to the maintenance here is an amazing feeling.”

By creating a virtual representation of an asset in the field using lightweight model “Digital Twin” visualisation, and then capturing info from smart sensors embedded in the asset, you can gain a complete picture of real-world performance and operating conditions. You can also simulate real-world scenario conditions for predictive operations.

Advances in virtual prototyping spaces has made possible the capability to simulating visual fidelity to a very high level. The next big challenge for virtual prototyping teams is simulating realistic interaction. Virtual prototyping, sometimes referred to as digital prototyping, is widely adopted by industry to simulate visual appearance and functionalities of production.

But conventional virtual prototyping techniques lack the simulation of the physical properties of a real interaction between user and product. Force feedback is based of development of virtual prototyping.

Virtual Reality tech creates an alternative reality in which worlds, objects and characters can be experienced that may not yet be available in reality so stakeholders are allowed to not only see the future product- achieved with concept sketch or mockup, but also experience the product and the interactions with its use context.

Simulation models as used in virtual engineering during development of training systems can be used during operation phases as well. In order to fully benefit from this, the simulation model must be connected to the physical system and other business operations In this way, information regarding past operation and current status can be fused with information regarding possible future operation, explored through virtual scenarios.

The overall result can be used for decision support in for instance operational planning or service and maintenance. In this way, simulation serves as a tool for arriving at a situation in which the future scenarios are perhaps not completely known, but in which one can readily address the most likely scenarios in an adequate manner.

Artificial intelligence can play a role in virtual manufacturing by improving simulation models or by offering better decision support. Extending the use of simulation models from the design phase to the operation phase also has advantages when new products are to be introduced or system needs to be reconfigured.

Virtual Reality is an attractive option since it offers the user a sense of being immersed in information where objects have a sense of ‘presence’ and allows them to interface with information at full scale if required. A design begins with an image or idea and the concept is disseminated via diagrams and descriptive speech.

Typically, information sources for conducting various virtual reality activities are not one single specific source, but instead all the different tech training information systems that are used in DoD The integration of these sources is not usually out-of-the-box-solution but most often highly customised solutions, engineered by specialists.
　
"Digital Twins" Provide Line of Sight to 3D Print Part Builds Previously Not Visible.
Digital Twins are learning digital models of physical assets, parts, processes and even systems. The purpose of the Digital Twins is to relay data about the performance and properties of a physical counterpart. With this information, Digital Twins will achieve complete repeatability of a 3D printed part, and greatly improve process reliability.

Now we have a digital representation of what the designer/customer wants, we have the actual part that we can touch and feel and also a Digital Twin of that actual part. In 3D printing we can only work in the digital world with a 3D digital model of the desired component. Now we can build the part, according to the 3D model, take that physical component and carry out our own 3D scan, creating yet another 3D model.

Digital Twin of the actual part can then be sent back to the designers and he can compare what we have manufactured to what his model wants, and even use the actual part model to simulate its impact, digitally, in the final design.

In the case of a 3D printer, we’re building a Digital Twin of a build process and recording the slightest defects, deviations and other build characteristics. With Digital Twins, models will continually be updated with each new build and become ever smarter in recognising and troubleshooting any potential issues that might arise.

Not only will there be a Digital Twin of the component, showing the internal and external requirements, but also a Digital Twin of the process that made that part; the process parameters, how long did the build take, how many layers were built, were there any issues.. all of these aspects building a digital picture of the part enabling further analysis and confidence in final applications of components.

Next generation of Digital Twins incorporate information from other sensors monitoring the 3D printing process, such as the shape of the pool of metal rendered molten by the laser. In addition, this smart, real-time quality control will not function in isolation.

The power of Digital Twins is their ability to share insights with each other. So you can imagine many 3D print machines sharing unique build insights with each other that makes them each more informed about what to watch for during a build process.

Through the Digital Twin process, you can accelerate the production of mission-critical equipment. Using Digital Twin technology, we’re aiming to rapidly speed up the time that parts could be re-engineered or newly created using 3D printing processes.

The key challenge with 3D printing is being able to additively build a part that mirrors the exact material composition and properties of the original part that was formed through subtractive measures. With operation of mission-critical parts there is no room for deviations in material performance or manufacturing error.

Properties and serviceability of 3D printed components are affected by their geometry, microstructure and defects. These important attributes are currently optimised by trial and error because the essential process variables can’t currently be selected from scientific principles.

A solution is to build and validate a Digital Twin of the 3D printing process capable of predicting of the spatial and temporal variations of physical parameters affecting the structure and properties of components.

In principle, the Digital Twin of 3D printing process , when validated with accurate with experimental data would replace or reduce expensive, time consuming physical experiments with rapid inexpensive numerical experiments. In the initial phase, the Digital Twin would consider all the important 3D print process variables as input and provide a transient 3D model.

1. Systems design: Design before you build with a visual, simulation approach.

2. Asset-based system of system design: Specify, publish, find, and reuse organisation simulation systems,

3. Product-line engineering: Design product platforms and variants quickly and efficiently, and make better trade-off decisions.

4. Systems model review: Improve product quality and model consistency through early design reviews within a systems modeling tool.

5. Systems model simulation: Validate complex behaviour earlier in the design life cycle, and establish predefined standards and best practice–based process.

6. Establish an open, flexible simulation system: Such a system is necessary to incorporate information sets from multiple engineering domains and quality control

7. Align combat engineering teams for better collaboration: Disconnected combat engineering teams across mechanical and electrical systems working in their own workgroups must collaborate as needed-- utility of systems-level view of products must be evaluated

8. Balance vitality and stability: Balancing vitality of innovation with reuse and predictive stability during establishment of an innovation platform for simulation and during product design and engineering.

9. Unify simulation connected systems optimisation: A single view of cross-domain system, product, and process is required for successful simulations

10. Incorporate quality with design and development: Achieving high level of product quality is why simulation virtually validates systems-level view. Assuring Incorporate/embed quality information from the early-stage design through subsequent product phases is key so simulations can more easily flow from system designs into product attributes.
　

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Top 50 "Digital Twin" Virtual Prototype Questions Combine Connected Product/Network Platform

12/11/2018

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The powerful utility of Digital Twins in Prototyping enables you to answer many fundamental questions without any physical prototype or testing. Everything is digital. So you are getting smarter about the operation of this product without spending lots of money to build anything physical.

With respect to developing products linked to networks, we’re in the early stages. But soon, some companies are going to want to sidestep development problems they’re experiencing. They’re going to realise that building and testing five rounds of prototypes is unacceptable. They’re going to realise months of delay completely undermines their competitive position in the marketplace.

As a result, those companies are going to want to adopt more proven and standardised practices. Virtually prototyping with Digital Twins is not there yet. But given the rush of companies toward network solutions, you should expect the demand for this practice to only increase.

The concept of a Digital Twin is mostly applied to the case where more insight is obtained from a physical operating product and a network platform. But in the case of virtual prototyping, the Digital Twin concept is applied to an old practice in mechanical hardware.

One approach to developing a smart connected product, one that hooks up with a network platform, is to just build it. Just piece it together. Throw some sensors on a product. Wire that to some kind of embedded system. Wire that to your antenna. Start sending data to an network platform. You and your organisation can actually learn a lot from going through that exercise.

While that needs to be done, you will quickly run into limitations on the experiments you can conduct with physical prototypes. Swapping out a sensor isn’t easy when it’s soldered in place. There might not be room, physically, for the sensor you really need for accurate measurement. You might run into too much electromagnetic interference for the antenna you planned to use.

Working through these issues is new to some organisations as they transition traditionally mechanical products to smart connected ones. However, the problems associated with resolving issues through physical prototyping isn’t new. In fact, that is an old concept when it comes to hardware. Long ago, mechanical and electrical engineers figured out that modeling and simulating a design virtually means you are more likely to get it right the first time when you get to prototyping and testing.

The benefits of an approach utilising virtual prototyping with digital twins are many. You have fewer rounds of prototyping, saving money and time. You have fewer change orders. You stay on schedule. You stay on budget.

So while virtual prototyping is new to some organisations, this approach has advantages when applied to the development of linked smart, connected products and networks platforms. Digital Twins are a key enabler.

How exactly can Digital Twins be used to virtually prototype smart, connected products and network platforms? You first need to set up the digital model component of a Digital Twin with one of the following:

Numerical Models: These models use machine learning and artificial intelligence tools. These applications or agents either extrapolate that data and/or correlate data to existing events. Both are an effort to predict future behaviour.

1D Simulation: These models are a combination of flow diagrams with equations or formulas behind the blocks that simulate the performance of embedded tools or multi-disciplinary engineering systems. These models can provide deeper insights into ongoing operation

3D Simulation: These models, often in the form of multi-body dynamics, are commonly used to predict the dynamics and structural performance of products. These models can provide deeper insights into ongoing operations.

For scenarios based on engineering physics or asset operation, no prototype or operating product exists. As such, there is no sensor data to feed this digital model. However, the model can be fed historical sensor data from prior products or even from past physical tests or operational data. In the worst case, a set of inputs can be modeled using statistics or even a higher level simulation, such as a multi-body dynamics model. This creates a set of input data that can be fed to the digital model.

The combination of that digital model and the input is enough to get started. You run the model as a simulation, generating data from virtual sensors, which are points of measurements from the simulation.

In this application, that virtually generated data is used instead of physically recorded data from sensors. That output can then be fed to a network platform as if it were receiving streaming data from a running product. Only, in this case, there is no physical product. There is only a virtual product that is running in a simulation.

In this scenario, you overcome many issues that you might experience when trying to physically prototype a smart, connected product.

You can change anything related to the sensor configuration, including placement or type.

You are not limited by network bandwidth other than the limitation between the compute resource running the Digital Twin and the one running the network platform.

You can change the product design in terms of mechanical or electrical hardware, embedded systems and more.
There is a tremendous amount of flexibility with this approach.

So now you have this concept of using a Digital Twin to virtually prototype a smart, connected product that is linked to a network platform. What does that get you? Interestingly, it allows organisations to answer a set of serious questions.
　
1. Is the systems configuration right for this product?

2. Do we need to use a physical sensor to capture this data, or can it be a virtual sensor?

3. Is the data we want to flow to the network platform limited by bandwidth?

4. Is edge processing required for the sensor data?

5. What data should be processed on the product versus being fed to the network platform?

6. Are there changes that should be considered to improve placement of sensors?

7. Are there changes that should be made to avoid interference?

8. How will connected product and network platform work to fulfill requirements?

9. What conclusions can be drawn from the data once it is in the network platform?

10. What data trends are precursors to events critical to the connected product?

Top 10 Operating Mechanisms of “Digital Twin” Architecture Framework Represent Flow Between Each Builder Module and Sub-System

To ensure digital networking of production systems and the optimisation of material-specific requirements, we need to measure, assess and replicate the changes in material properties in a process where "Digital Twins" of materials are created.

The materials digital space has laid the groundwork for this process. When a finished part rolls off the production line, this is one of the first questions always asked: "Does this component have the properties we want?"

Often, even the tiniest of variations in the production environment are enough to alter a part’s material properties – and throw its functionality into question.

Manufacturers avoid this by close inspection of samples throughout the production process. Breaking down the samples into their composite parts and measuring them separately is an extremely time-consuming process.

"The outcome of the sample testing process branches out into an array of different subsets, each with their own specific measurement results. While experts may be able to keep an overview of the complex interrelationships in their heads, until now there has been no way to take the diversity of resulting data and portray it in a coherent digital format."

Now, for the first time, a proof of concept has been developed demonstrating that it is possible to digitally represent many such material processing cycles with a materials data space for test specimens produced using additive manufacturing.

"The data space concept allows us to integrate any type of material information into a digital network – a really valuable tool. We want to use the materials data space to automatically generate a digital twin of each material that will mirror the current state of the physical object under examination."

Data spaces can be used to integrate all types of materials information into digital networks. The advantage of the materials data space is that it provides an overview of all relevant parameters at a glance, whereas formerly data on different material parameters was scattered among numerous data repositories in many different formats.

But the real promise lies in the future. "In the years to come, the materials data space has the potential to become the production command center. Whenever component quality isn’t up to the expected standard, you can compare it with information on previous components stored in the materials data space to determine whether the present component can in fact be used or whether it must be rejected.

In the future, these results could be automatically integrated into industrial decision-making processes: whenever component quality dips below the required standard, production automatically comes to a halt.

Creating the data space –and managing the diversity of materials data – calls for a corresponding information model. "In this case, the model reflects the natural material world, in which material states and properties are assigned to defined categories.

The best way of thinking about it is in terms of a social network where each user is a node in the network. And in turn, these nodes have their own subject matter associations. What we do is to create semantic relationships between the individual material objects and their associated processing steps.

Then there are also interrelationships among these communities. What would be a “follow” on social media is represented in the materials data space by details on the chronological sequence of production or work steps, for instance "leaving the additive manufacturing process" or "this laser is part of the 3D printing process".

The new demonstrator for additively manufactured metal components has the capacity to generate samples, characterize the materials they contain, conduct subsequent data analysis and determine material properties.

Thanks to the logic underpinning the model, users can make extremely complex queries of the data space that simply wouldn’t be possible with the same degree of flexibility in the case of a conventional database.
　
1. Creation of quality prediction models: Collected sensor/process network data crunched with AI techniques to build quality prediction models

2. Storage of quality prediction/response models: Response plans/times are entered into model repository according to operator experience

3. Creation of production schedules: After production simulation schedules include machine/production target outputs specified to timetable and sent to reference coordinator builder to lead into quality/productivity detector

4. Request for reference performance indicators: Coordinator delivers the production schedules to performance indicator simulation sub-system to be used in production-monitoring standards

5. Transmission of reference performance indicators: Reference models created based on simulation results serves as the criteria for manufacturing execution monitoring criteria to include target quality and production per unit time per process

6. Quality analysis and prediction: Quality and productivity detector predicts the quality and productivity in real time using quality prediction model and reference performance indicator

7. Request for analysis of response plans: Coordinator requests model repository for analyses of response plans for irregularities and receives corresponding information

8. Request for future performance indicators: Predict future performance indicators caused by responses to irregularities and current schedule sent to the performance indicator simulation sub-system.

9. Request for new schedules: Coordinator requests new schedule when difference between change in initial/future performance due to irregularities is significant

10. Transmission of visualisation information: Progress of entire systems is simultaneously sent to dashboard in the form of information visible to the user through graphical user interface.
　
Top 10 “Digital Twin” Engineering Team Strategies Define Agent-Machine Interaction Focus Configuration Programme Goals

1. Formalise planning development, integration, and use of models to inform enterprise, programme decision making, support engineering activities to digitally represent the system of interest

2. Ensure models are accurate, complete, usable across disciplines to support communication, collaboration and performance and decision making across lifecycle activities

3. Provide enduring, authoritative source of secure authentication with access/controls to establish technical baseline, product digital artifacts, and support reviews for accurate decision making

4. Incorporate technological innovation to establish end-to-end digital engineering enterprise and foster conditions for productive step advances towards goals

5. Enable end-to-end decision making using advance human-machine interactions

6. Establish mature supporting digital engineering activity infrastructure to perform activities with connected information networks

7. Develop, mature, and implement technology tools to realise digital engineering goals and share best practices using models to collaborate with stakeholders

8. Improve digital engineering knowledge base, policy, guidance, specifications, and standards and streamline contracting, procurement and business operations

9. Lead and support digital engineering transformation efforts, vision, strategy, and implementation to establish accountability to measure and demonstrate results across programmes

10. Build and prepare workforce to develop knowledge, competence, and skills with active participation and engagement in planning and implementing
　
Top 10 Observations of Day-to-Day Work/Field Experiences Identify Trends/Challenges of “Digital Twin” Simulation Utilisation

Specific sessions with engineers, technical/simulation managers, R&D, quality managers were performed. Surveys were conducted in a typical inductive approach assess key practices, processes, tools and data associated with simulation and product/process development.

1. Domains/application: depth and completeness of engineering simulation areas

2. Methods: engineering simulation tools utilization

3. Level of integration within driver/follower processes

4. Process gates and decision criteria: definition, completeness, visibility

5. Documentation of simulation process and decision-making criteria/milestones

6. Level of adoption/dissemination of engineering simulation in extended organisation

7. Depth/completeness of specific skills of engineering simulation

8. Organisation: relationship and integration between engineering simulation teams and the rest of product development teams

9. Data lifecycle/workflow: modelling, capture, revision, access/control

10. Infrastructure: central/distributed computational capacity, support/availability, post-process remote/local capabilities

Top 10 “Digital Twin” Implementations

1. 3D model of a generic part/product

2. 3D model of a specific part/product

3. 3D model shows live information

4. Live set of physical database assets

5. Process simulation validate manufacture potential

6. Human Machine Interface process plant

7. Live icon representation e.g. display indicator

8. Live numerical value graphic

9. Simulation of a machine e.g. rotating

10. Simulation responding to real time data

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Top 10 Structure Assessment Tool Requirements Provide Evaluation of Status Update Framework

12/1/2018

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Current readiness systems only include commander’s best estimate for equipment status. Estimates have traditionally been utilised usually for overall equipment assigned to the unit and not individual pieces of equipment.

Military Services use systems to maintain records of equipment under service, but records do not include any information about what units it is assigned to.

Central to the work presented here was the development of a tool, the Marine Air-Ground Task Force Equipment Structural Assessment, which was loosely based on previously developed plan

Inputs to the system consist of a MEU equipment list, the tasks identified through the mission deconstruction process, the measures and metrics used to define equipment capabilities, and the set of linkages between tasks and equipment.

What equipment is available to the MEU to accomplish mission tasks and subtasks? A diverse set of factors affect the types of equipment aboard a MEU, including not only space available but also risk trade-offs made by commanders and expectations about the mission on deployment.

Since there is no standardised table of equipment for a MEU, the study team obtained a list of equipment assigned to a recent MEU, which included information on what was embarked and what was left behind.

What measures and metrics should be used to assess the capability of selected equipment? The loading list provided the set of available equipment. We then used equipment manuals and sponsor input to define the capabilities of each piece of equipment in performing designated tasks. This information is displayed to the user when a piece of equipment is selected

We identified the measures and metrics, or “planning factors,” needed to assess the capability of each piece of equipment in the loading list. In our initial construction of the tool, we identified which alternative equipment might accomplish a task, but not as effectively.

We concluded that either we had it wrong or that other equipment might do just as well or better than what we selected. Consequently, equipment selection is now up to the user of the tool. The upgrades proceeded in parallel with these activities.

We have not received official Marine Corps approval to run our application at Job Sites. The programme is a valuable materiel readiness information tool designed and Tested for Marine Corps.

Users training on existing systems, even though we have designed logical architecture and operation modes, we have found concerning lack of training on logistics information tools throughout Marine Corps.

Tool supports this objective by asking the user to define mission-specific characteristics and allowing the user to tailor equipment lists, equipment priority, and task priority as appropriate.

The approach used in this report is for the user to use the tool to facilitate the development of planning factors by the user and use the tool to assign equipment to tasks.

This process provides a framework that MEU commanders can use to develop mission plans and understand where equipment shortfalls are likely. The process consists of simple steps that translate mission requirements into tasks, subtasks, and military activities, each of which is linked directly to the types of equipment needed for completion. It also highlights key parameters that may affect the types of equipment needed or the execution of key tasks.

Site Visit Executive can first look at broad readiness, but can also look at readiness levels of subordinate units to provide the ability to control, distribute, and replenish equipment and supplies in assigned areas of operation, to receive supply support from and provide supply support to other services

Readiness Terms are used in different contexts/processes. Operational gaps in systems used by Marine Units must be closed so exchange is seamless. Capability to link information as it is processed by Units must be built.

Aggregated information provided to Commanders must be traced/linked to operational systems used to rollup information. But no Marine Site Executive has yet stood up to identify functions spanning across process and write terms required to support processes

If Site Visit Executive has better overview of equipment status, resources will be allocated/pooled more efficiently so greatest potential for operational readiness is realised.

Information from readiness systems is required to determine number of pieces of equipment available for deployment. No Site Executive has created an easy way to link equipment information available from readiness and Services systems.

Technological advances in production and distribution can strengthen the Navy and Marine Corps aviation parts supply chain. Improved spare parts logistics systems and 3D printing will increase flight availabilities and decrease costs. 3D printing is the headline of how far we’ve come with efficiencies, both at the fleet readiness centers and out in the field.

The entire spare part logistics system has the potential be sped up with the use of 3D printing. With forward deployed forces, addition of 3D printing increases availability and save costs by quickly producing small replacement parts onsite instead of waiting for the supply chain to send equipment far off.

In addition, 3D printing as a way for the industry to quickly manufacture the parts needed by aircraft maintainers without necessarily having to sink money into new machinery to make specialised components not frequently requested.

Ultimately, this on-demand manufacturing will help companies control their costs. The only limiting factor is the ability for 3D printers to create air-worthy parts. “We’re at the front end of this. There are parts that require airworthiness for approval and the non-air worthiness, the non-airworthiness are easier to do.“

Maintenance portion of an aircraft program is of equal importance as new acquisitions in keeping costs down. “We got to operate it, and sustain it, and fly it for the lifecycle. So understanding your supply chain and making sure it’s robust is key.”

A new logistics sustainment system Marine maintainers are trying will help both the service and industrial base adjust their ability to purchase and manufacture replacement parts. The new system prioritises how to allocate replacement parts to aircraft based on how quickly it will return to service after the part arrives.

Consider the fate of two aircraft from different squadrons. Both are grounded, and each requires the same replacement part, but one of the aircraft needs additional other work done to get back in the air.

Under the current system, the part goes to the maintainers who request it first, even if this aircraft needs additional work resulting in being grounded for weeks. Meanwhile, the aircraft that only required the one part could’ve been ready sooner, but remains unavailable while waiting for part delivery.

“We’re now using supply optimisation tools that are taking a look across a base, and not only a base but across a type, model series. As an example, a long lead-time part is coming in, so what airplane benefits most from that? That’s one area where we’re using agent learning to make decision making.”

We got the chance to learn about the latest technologies showing potential for Marine Corps deployment. We are covering this expo as a team who has never been to one, so here is our perspective on the experience.

Even though we had never been to a Marine expo before, we had a pretty good idea of just what it will entail. We were expecting multiple companies to be set up in theatre showing off their latest tech advancements and best manoeuvre practices.

These expos seem like a great way to learn about Marines and connect with one another for possible future partnerships or work. In a nut shell, we believe this advanced expo is going to house a lot of tech and experienced Marines and we should be able to learn a lot from it.

When we walked through the doors and entered this expo, it seemed like the number of Marine suppliers was infinite. Everywhere you looked there was something new to look at and learn about. The first supplier that grabbed my eye was a growing company from who had designed a sorting system for small parts.

The group realised their designs potential for larger parts and how it could be applied to multiple different industries. The group upscaled their design and went from one machine designed for extremely small parts, to an array of multiple machines each having the ability to sort different sized part much larger than the first design. It was incredible to see how one design could be changed only slightly and have so many different applications for Marines.

There were no limitations when it came to suppliers and the number of different AORs present at the expo. There were solutions for warehouse storage, automation, sorting, milling bits, and even 3D printing. One of our favorite booth we visited was run by a company who had all the 3D printing solutions you could ever need, even for new areas of the field.

These suppliers were displaying their new desktop metal 3D printer and its ability to print in metal.3D printing in metal was something we had heard about before, but imagined it had only been done by a very small number of Marine companies.

Right there in front of us this printer was creating quite incredible parts all in metal. We were told these prints had similar strength qualities as cast metal parts and were printed using a type of metal powder mixed with wax.
This was incredible to hear if you had only ever experienced 3D printed plastic parts before. Looking around the booth it was easy to see all the Marines gears turning as they were able to see all the applications this desktop metal printer could be used for their own field operations.

Going into this expo we didn’t have much knowledge about Marine manoeuvres. After speaking with suppliers showcasing part sorting/packing systems, we were surprised to learn a significant amount about sensor sorting systems. These machines simply take images of the sorting bed and use tools to tell which items to grab or re-sort.

We saw how easy 3D printing in metal and its benefits compared to plastic. With this machine being able to create custom metal parts that no other manufacturing process can create, it is easy to see the endless possibilities and applications for this process.

An expo is a great place to satisfy our curiosity. With all kinds of new technology and processes you can check out all the booths for hours finding the answers to all your pressing questions about the Marines.

These expos don’t need to be just for Marines. We were welcome to learn a little more about the processes and machines used to make products. Overall, going to this advanced expo was a great experience and we will be attending many more in the future.

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 sophistication 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.

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 sophistication, 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.

You don’t have to be a part of a high profile AI initiative to find value in the science 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.

“That is a capability and it leads to benefits that are tremendous. And the possibilities may be endless. There’s an easy answer to the question: Where can AI be applied? “It can be applied literally everywhere.

“The biggest part of the problem of artificial intelligence is: they build these incredibly long algorithms with all of these gates to go through. They push all of this machine learning and data through it. Frankly, we are not entirely sure how all of that works, all the time.

1. Use language processing and machine learning to automatically classify and match incoming data to indicators and warnings being monitored. Provide alerts on trending topics, keywords or themes that may indicate emerging tactics, techniques and procedures.

2. Display both geographic and temporal representation of multi-intelligence data with a natural language generated summary of the data. Include the ability to break data into individual entities as needed and internalise analyst annotations into the automated summary.

3. Automatically map finished intelligence products to the priority intelligence requirement to help answer with automated caveat classification of documents tied to user permissions. Include smart search capabilities to that repository so analysts can find relevant products more efficiently.

4. Monitor human developed courses of action beside computer generated courses of action including the criteria for suitability, feasibility, acceptability, uniqueness and completeness. A machine will see information differently than its human counterparts and may identify behaviour discrepancies present in data that human analysts may miss due to the sheer volume and complexity of reporting that an intelligence analyst is presented with.

5. Capture workflows and product development in a shared space so knowledge gaps are reduced between shifts or rotations. Use automation to track knowledge gaps and alert users to update analysis and finished products when significant knowledge gaps are filled to tag/map intelligence gaps as new information comes in and alert users to the new information.

6. Measure impact of operational intelligence and associated collections or requests that contributed to that intelligence by automating inputs and processes serving as operational measurements.

7. Add cognitive search into the massive data repositories analysts are required to sift through to move beyond keyword search and enable contextual search at an enterprise level.

8. Provide in-depth training on AI systems and set standards on how technology augments the human analytic process without replacing the analyst behind the screen. In short, tie the technology into existing workflows and adjust workflows to account for technological innovation.

9. Conduct initiatives in parallel with operations to ensure force efforts are complimentary and requirements are aligned with discrepancy alerts or gaps in the operational plan and the intelligence needed to execute it.

10. Invest in both garrison and tactical systems and infrastructure that are capable of running and sustaining the increased computing power that comes with training and deploying AI programmes

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Top 10 Blockchain Rules Consider Work Order Consensus Agent Cooperation Bucket Brigade Routes

12/1/2018

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We set up experiment involving Digital Twin” robots learning to work together: one robot ideally handing off items to the other, which in turn carries them to a final destination.

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 would speed up the training process and implement robotics controller.

Bucket brigade systems provide guidance shortening the number of cycles required to learn task rules using only a few training examples starting with classifiers that were randomly generated.

The Remote Access Nondestructive Evaluation system is a snake-like robotic arm tool that fits into small spaces of an aircraft to perform inspections. Maintainers usually have to remove whatever hard-to-reach component and crawl inside the small area.

The robotic arm can manoeuvre through access ports with small as diameter and serve as an agents eyes as it moves around inside the aircraft, saving time and eliminating the need to take the aircraft apart. The robot is currently a prototype and is ready for the programme office to request exact specifications for use on certain aircraft.

New Platform Creates Autonomous Robots Create Map of Workspace Using Sensors so Users Choose/Teach Route

Platform integrated into commercial service robots so autonomous navigation stack doesn’t have to be built from scratch. This is interesting stuff that is potentially game-changing in terms of the cost able to command volume pricing on sensors that we can’t. And we are engineering things to fit together nicely.

Getting tools right is especially challenging for robots that will be autonomously navigating in complex scenarios such as airstrips and other high-traffic workspaces. These areas often have tight spaces and continuously changing obstacles that require complex routes. The challenge is creating tool to handle these issues with the end-user in mind.

The robot must involve minimal training for operators, no battle space setup, single-shot learning by demonstration, and productivity reporting.

Variables impacting autonomous navigation are not limited to physical obstacles crowding a robot’s work space. Feature-less work space, and even time of day, add complexity to autonomous navigation. Many of these types of hurdles are edge cases that do not present themselves until after tools have been developed and the robots are tested in a live mission space. Edge cases are the punch you don’t see coming.

For example, being able to navigate in a cluttered, dynamic workspace with a lot of troops moving around is difficult because there’s a lack of features. There’s nothing, really, to anchor or tie into when you’re building your map.”

Success is contingent upon getting your robot, and the tools that runs it, into many different workspaces early on. Functional autonomous navigation systems are not developed in a lab. It’s fine to begin development there to create a demo, or to get funding. But those stages are the limit for lab testing.

It won’t work until you’ve been in a number of scenarios because the problems that you’re going to experience in theatre-specific deployments cannot be replicated in a lab. You can’t solve or anticipate every edge case your robot will encounter in the real world. Keeping the troops involved in the installation process and giving them the tools to troubleshoot issues in real-time can improve your robot’s efficiency.

Examples include when robot mistaking light from a reflective surface as a physical object or infrared heaters disrupting the robot’s path. Essentially, the more edge cases you can solve, the better your navigation solution.

The key to designing a robot with autonomous navigation is creating a system that has precise and accurate motion control.

“For a lot of robots, you’re just taking a robot from point A to point B, but with field applications like equipment distribution machines, they need to drive as close to an edge, as close to a wall, as close to an obstacle as possible to maximise the floor coverage provided.”

Highly accurate motion control is imperative if you want your robot to be able to handle complex, tight spaces. That’s something you can’t do with a robot that has much larger footprint. Designing a system that is as tight and accurate as possible give you much better capabilities to navigate complex spaces.

Detection by actual troops is crucial for expanding end-user applications. If your robot can’t tell a set of troops from a package on the floor, you’ve hamstrung your unit manoeuvre before it starts.

“If you’re developing your own tools, if you are looking for navigation systems to use in your robotics project, then having a system that can recognise Troops as different to obstacles is essential.

Unless you’re going to clear everybody out in the space the robot works in, which limits the applications, you really need to solve the workforce element. This is one of the biggest problems you need to solve.

But being over cautious also has its problems. In running some initial pilot tests with the equipment distribution machine, the robots were checking, pausing, and analyzing for the sake of safety so often it made Troops less comfortable around them. People thought the robots weren’t intelligent, thus making them feel uncomfortable around the robots. It’s critical that you use sensor data from real-world scenarios and virtual space to reduce false positives.

For your product to be scalable, the installation process must be simple, not technical. Many of today’s robots require an engineer for installation into new space since process is simply beyond the skillset of non-technical staff. This in-depth and technically complex launch can bottleneck this critical early stage; having an engineer sent on-site to every new Troops customer is not sustainable or scalable.

One way to counteract this challenge is to have your customers identify workforce who might be capable of taking on installation as a new project. Another is to do some preventative maintenance regarding design. You want your robot to be familiar to products your customers have used before. Make sure the user interface is lean and intuitive.

“We wanted to keep it as simple as possible… As you can see in this screenshot right here, there are only two choices for the user: Choose a route or teach a route. The user can use the machine the way they always have and while they are doing that, it creates a map of the space and records the routes, or they set to play.”

“Trusting” Robotic System to Make Quality Parts Opens Door to Build Usable Parts When and Where You are Working
Consider sustainment and how a maintainer can print a replacement part at sea, or a mechanic print a replacement part for a truck deep in the desert. This takes 3-D printing to the next, big step of deployment.

We are exploring how machine learning and artificial intelligence can make complex 3D printing more reliable and save hours of tedious post-production inspections.

In modern factories, 3D printing parts requires persistent monitoring by specialists to ensure intricate parts are produced without impurities and imperfections that can compromise the integrity of the part overall. To improve this labor intensive process, we are developing multi-axis robots that use lasers to deposit material and oversee the printing of parts.

Initial work will focus on developing computer models that can predict the microstructures and mechanical properties of 3D printed materials to generate simulation data to train with, looking at variables such as, the spot size of the laser beam, the rate of feed of the titanium wire and the total amount energy density input into the material while it is being manufactured.

This information helps the team predict the microstructure, or organisational structure of a material on a very small scale, that influences the physical properties of the additive manufactured part.

Information will be plugged into a model that predicts the mechanical properties of the printed component. By taking temperature and spot size measurements, the team can ensure they accurately controlling energy density, the power of both the laser and the hot wire that goes into the process.

All of that is happening before you actually try to do any kind of machine learning or artificial networks with the robot itself. That’s just to try to train the models to the point where we have confidence in the models.

One key problem could come in cleaning up the data and removing excess noise from the measurements. Thermal measurements are pretty easy and not data intensive, but when you start looking at optical measurements you can collect just an enormous amount of data that is difficult to manage.

We want to learn how shrink the size of that dataset without sacrificing key parameters, compressing and manipulating this data to extract the key information needed to train the algorithms.

Robots will begin producing 3D titanium parts and learn how to reliably construct geometrically and structurally sound parts. This portion of the program will confront challenges from the additive manufacturing and AI components of the project.

On the additive manufacturing side, the team will work with new manufacturing process, trying to understand exactly what the primary, secondary and tertiary interactions are between all those different process parameters.

As you are building the part depending on the geometric complexity, now those interactions change based on the path the robot has to take to manufacture that part. One of the biggest challenges is going to be to understand exactly which of those parameters are the primary, which are the tertiary and to what level of control we need to be able to manipulate or control those process parameters in order to generate the confidence in the parts that we want.

At the same time, AI machine learning challenges need to be tackled Like with other AI programs, it’s crucial the communication interface is learning the right information, the right way. The models will give the communication interface a good starting point, but this will be an iterative process that depends on the communication interface ability to self-correct.

At some point, there are some inaccuracies that could come into that model so now, the system itself has to understand it may be getting into a regime that is not going to produce the mechanical properties or microstructures that you want, and be able to self-correct to make certain that instead of going into that regime it goes into a regime that produces the geometric part that you want.

With a complete communication interface that can be trusted to produce structurally sound 3D printed parts, time-consuming post-production inspections will become a thing of the past.

Instead of nondestructive inspections and evaluations, if you have enough control on the process, enough in situ measurements, enough models to show that that process and the robot performed exactly as you thought it would, and produced a part that you know what its capabilities are going to be, you can immediately deploy that part.

That’s the end game, that’s what we’re trying to get to, is to build the quality into the part instead of inspecting it in afterwards.

Confidence in 3D printed parts could have dramatic consequences for soldiers are across the services. As opposed to waiting for replacement parts, service members could readily search a database of components, find the part they need and have a replacement they can trust in hours rather than days or weeks.

Advanced Weapons System Sustainment Team Strategies Utilise New Workflow Tools to Improve Tracking of Product Requirements

While sustainment strategies do not guarantee successful outcomes, they serve as a tool to guide operations as well as support planning and implementation of activities through the life-cycle of the aircraft. Specifically, at a high-level the strategy is aimed at integrating requirements, product support elements, funding, and risk management to provide oversight of the aircraft.

For example, these sustainment strategies can be documented in a life-cycle sustainment plan, postproduction support plan, or an in-service support plan, among other types of documented strategies.

Additionally, program officials stated aircraft sustainment strategies are an important management tool for the sustainment of the aircraft by documenting requirements that are known by all stakeholders, including good practices identified in sustaining each aircraft.

Agents are integrating time-saving applications into their own workflow with proven concepts across commands and services. These time-saving tools enable simultaneous access to data such as requirements, program notes and contractor documents.

You can move from a system where you’re waiting for status updates with comments to come back from an engineer or contractor to logging into a system where you can see what they’re working on, in real time, so team can work on a project without any lag time.

Acquisition rules require contractors, program managers, engineers and other stakeholders to work together but it usually involves dozens of technical documents to move from contractor to DoD on any given day, creating a nightmare for acquisition officials charged with ensuring each document is reviewed and meets expectations

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.

“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. Every agent in the “Blockchain” can see, through comment tracking, how data changed to capture the correct info and meet requirements.

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.

The parallel 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. It is shown that 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.

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, effective strategies for the further tuning toward large scale computing should be discussed

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. The process of changing agent consensus rules can be rather difficult for any project and requires some time.

We show that collaboration is achieved only when robots are rewarded based on a non-discounted global reward averaged over time, concluding with work in agent modeling under communication.

We have used communication protocol for agents to subcontract subtasks to other agents. In this approach, each agent tries to decompose tasks into simpler subtasks and broadcasts announcements about the subtasks to all other agents in order to find “contractors” who can solve them more easily.

When agents are capable of learning about other agents’ task-solving abilities, communication is reduced from broadcasting to everyone to communicating exact messages to only those agents that have high probabilities to win the bids for those tasks.

A related approach is presented where learning is used to incrementally update models of other agents to reduce communication load by anticipating their future actions based on their previous ones.

Case-based learning has also been used to develop successful joint plans based on one’s historical expectations of other agents’ action. Multi-agent learning is a new field and its open research issues are still very much in development. Here, we single out several issues we observed recurring while surveying past implementation efforts.

We believe these specific areas have proven themselves important open questions to tackle in order to make multi-agent learning more broadly successful as a technique.

These issues arise from multi-agent learning, and may eventually require new learning methods special to multiple agents, as opposed to the more conventional single-agent learning methods of case-based learning, reinforcement learning, traditional developments computation now common in the field.

Shaping, layered learning, and fitness switching, are not multi-agent learning techniques, but they have often been applied in such a context.

Less work has been done on formal methods for decomposing tasks and behaviours into appropriate for multi-agent solutions, how agents’ sub-behaviours interact, and how /when the learning of these sub-behaviours may be constructed in parallel.

Consider robot soccer as an example: while it is true that agents must learn to acquire a ball and to kick it before they can learn to pass the ball, their counterparts must also have learned to receive the ball, and to ramp up difficulty, opponents may simultaneously/co-adaptively learn to intercept the ball.

Not much attention has been paid to examine how to form these “decomposition dependency graphs”, much less have the learning system develop them automatically.

Yet to construct the learning process in parallel to simplify the search space, and reduce more robust multi-agent behaviours, understanding these interactions is important. One notable exception occurs that in many domains the actions of some agents may be independent.

Predator-Prey pursuit is one of the most common work spaces in multi-agent learning research, and it is easy to implement. Pursuit games consist of a number of predator agents cooperatively chasing a prey. Individual predator agents are usually not faster than the prey, and often agents can sense the prey only if it is closeby. Therefore, the agents need to actively cooperate in order to successfully capture the prey.

The goal of our leveled consensus “sustainment contracting” mechanism is to allow some fexibility as in the case with no commitment while guaranteeing agents some level of security as in the case of full commitment.

Full commitment contracts can be viewed as one end of a spectrum where commitment-free contracts are at the other end. Leveled commitment contracts span this entire spectrum based on how the decommiting penalties are chosen. Leveled commitment is desirable because it speeds up the negotiation process by increasing parallelism.

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.

1. Work forward and continue to pick units for your job on the forward line

2. When you exchange work with your successor, then work backward

3. If you are the last worker when you reach the end of the forward line transfer your job to the backward line and work backward;

4. If you catch up with your successor, who is crossing the aisle, then wait.

5. Work backward and continue to pick units for your job on the backward line

6. When you exchange work with your predecessor, then work forward

7. If you are the first worker to complete your job at the end of the backward line initiate a new job and work forward

8. If you catch up with your predecessor, who is crossing the aisle, then wait.

9. If you are on the forward line, remain idle until your successor finishes crossing the aisle, then work forward

10. If you are on the backward line, remain idle until your predecessor finishes crossing the aisle, then work backward.
　

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Top 50 Instructions to Rebuild Readiness Provide Regular Input on Status of Recovery Issues

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Top 10 Blockchain Rules Consider Work Order Consensus Agent Cooperation Bucket Brigade Routes

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