Our fixed sites face a wide area of security threats including traditional asymmetric weapons such as snipers, artillery, rockets & mortars, and more recently, the coordinated multi-agent “swarm” of low-cost drones. Other threats include physical incursion, vehicle-based improvised explosives, etc. Battlefield sensor technologies have been developed to provide threat reports to central command posts.
Typically, these systems are used to indicate the threat point-of-origin, and corresponding track if available. For area-target weapons, such as mortars, rockets, and RPGs, the point-of-impact locations may also be resolved. In many cases, the weapon type may be determined, which is vital to deploy countermeasures effectively.
Traditionally, sensor node locations are placed at fixed sites often constrained by availability of power and network communications. Once commissioned, surveillance systems are usually static, but the threat remains dynamic based on the conditions outside the fixed site and within the fixed site as critical assets are moved/relocated.
New directions for multiagent systems in wireless sensor networks are gaining increasing interest for military applications and potential frameworks have been proposed for dealing with the challenges of multiagent-based applications in the wireless sensor networks.
Efficiency of multiagent systems in wireless sensor networks prompts the use of emerging mobile software packets in different simulated approaches or real-world applications. Heterogeneous and distributed wireless sensor networks could be integrated with the multiagent systems to map the real-world challenges into the artificial intelligence world.
Other factors affecting sensor performance are also variable such as weather. Perhaps most importantly the enemy is adept at learning where surveillance systems are located and understanding capabilities and limitations. As the enemy learns, they are able to postulate means of avoidance and/or defeat.
Multiagent systems have been applied from simulated approaches like object detection/tracking, control/assistant, and security systems to real-world applications, including unmanned aerial vehicles. Furthermore, the integration of wireless sensor networks with multiagent systems have emerged novel applications like mobile robots. However, the extensive use of mobile agents in wireless sensor networks has posed different challenges for the military, including security, resource, and timing limitations.
Using the capability of the multi-agent technology in networks could enhance most of the military applications like target tracking, urban control systems, firefighter assistant, data aggregation, detecting and monitoring the events, and intrusion detection. Mmulti agent systems have become an essential part of the real-world applications of networks like Swarm Sense robotics-based systems.
A common drone swarm system could consist of two drones unmanned aerial vehicle/over thousands of drones. The required autonomy increased to control such systems without any manual pilots, when the number of drones in a swarm system exceeded a predetermined threshold. Therefore, it is vital to create the autonomous drones which manage themselves automatically, effectively, and robustly in any anti-access, bandwidth-limited, and area-denied environments.
Due to the interconnection of multi agent systems and wireless networks, group of drones can be enabled to cooperate and coordinate them to perform the missions automatically, which require a large-area coverage, immediate data processing, efficient deployment without exact pre-planning, and uninterrupted cooperation and coordination during the emergency operations.
This project proposes technology for wide area surveillance in-and-around fixed sites providing mobile, semi-autonomous sensors which may be continually relocated/repositioned in lieu of changing threats or environmental factors.
This topic envisions utilization of recent advances in cost-effective, semi-autonomous/robotic platforms and low-power multi-node communications technology. Drone-based technology could be leveraged for aerial surveillance and/or node relocation. It is recommended to seek utilization of existing mobile robotic platforms, keeping in mind cost per node, payload, mission run time, and other factors.
A multi-modal approach is required and this could include some combination of visible and/or infrared electro-optical, radar, ultrasonic, acoustic, laser, etc. This topic should foster concepts leveraging advances in the field of deep learning.
Event analysis in consideration of historical data can improve assessments of hostile intent. Especially when such events are detected by a number of sensors, across several disparate modalities.
Over the past few years, commercial off-the shelf hardware allows for “big data” processing on conventional desktop computers and laptops. New types of processors are being developed optimized for such computations. This topic proposes a boundary condition limiting the utilization of hardware feasibly used in a tactical operations center or similar facility in a desktop or laptop computer platform.
The objective of Phase I is to demonstrate feasibility of a semi-autonomous mobile multi-modal sensor network via study, simulation, and practical testing. The result should be a design which can be realized in Phase II providing detailed information on selected mobile platforms such as cost, power, payload, terrain capability, etc.
Based on the design established in Phase I, a system should be designed implementing a group of mobile, semi-autonomous agents accomplishing detection using a various sensor modalities. The system should be demonstrable in a relevant environment. A range of threats should be simulated demonstrating detection capability across several modes, allowing exploration of design tradeoffs discussed. Data exfiltration to other systems should be considered, to support activation of countermeasures.
Based on results from Phase II, the system will be optimized for commercialization and transition to military platforms providing new and improved wide area security capabilities for DoD bases.
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.
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.
Programme will have one or two dozen component agents and on the order of a hundred characteristic agents. In the current implementation, agents are not created, destroyed, divided, or fused during operation, but as the system matures, designers will need a way to add both component and characteristics agents to the community as a design is refined.
Agents communicate digitally, and currently use point addressing. Messages do not persist outside of agents, and agents do not move over the network. Fixed market protocol is used but also provides for the design engineers behind component agents to communicate directly with one another using standard work orders.
The initial configuration of component agents and characteristic agents is defined when the system is initialised, but component agents can engage in markets for other characteristics as the system runs.
Like any industrial project, it begins with requirements of the problem domain, and draws selectively from results of investigations to meet those requirements.
The flow of information is not unidirectional. 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.
Application problems in distributed artificial intelligence are concerned with finding a consistent combination of agent actions to be formalised as distributed constraint satisfaction problems involved in effort to find consistent assignment of values to variables distributed among multiple automated agents.
Distributed artificial intelligence is concerned with interaction, especially coordination between agents exhibiting auto behaviour. Since distributed network solution strategies are spreading very rapidly due to tech advances, commanders have pressing needs for distributed techniques in mission readiness determination.
Product Design is an issue of information processing when information characterises requirements for product to be converted into knowledge key to Prototype Trade-Offs about a product. One of the challenges designers deal with in product design is a lack of detailed information. At start of design process, less is known about the design problem at hand.
Agents buy and sell the various characteristics of a design. Each characteristic agent is a computerised agent that maintains a marketplace in that characteristic. In the current implementation, the agents representing components are interfaces for human designers, who bid in these markets to buy and sell units of the characteristics.
A component that needs more latitude in a given characteristic like more weight can purchase increments of that characteristic from another component, but may need to sell another characteristic to raise resources for this purchase. In some cases, models of the dependencies between characteristics help designers estimate their relative costs, but even where such models are clumsy or nonexistent, prices set in the marketplace define the coupling among characteristics.
Set-based reasoning is used to drive the design process towards convergence. Most design in industry today follows a point-based approach, in which the participating designers repeatedly propose specific solutions to their component or subsystem. The chief engineer is expected to envision the final product at the outset, specifying to the designers what volume in design space it should occupy and challenging them to fit something into that space.
In set-based design, tasks of the chief engineer is not to guess the product location in design space, but to guide the design team in a process of progressively shrinking the design space until it collapses around the product. Each designer shrinks the space of options for one component in concert with the other members of the team, all the while communicating about their common dependencies.
This approach directly reflects consistency rules for solving constraint problems. If the communications among team members are managed appropriately, the shrinking design space drives the team to convergence.
Agents represent entities in the shop. represent manufacturing resources such as machines. These domain-oriented agents are clustered into communities, and each community has several service agents: a bidding agent that handles all transactions among domain agents, a constraint propagation agent that propagates task dependencies and does some constraint satisfaction, and a meta agent that registers the skills of the domain agents in the community.
Many configuration systems do not allow manufacturers to collaborate on networks for offer-generation or sales-configuration activities.
But the integration of configurable products into the supply chain of a business requires the cooperation of the various manufacturers’ configuration systems to jointly offer valuable solutions to customers like contract type sequencing for satisfaction of task allocation with leveled agent commitments.
In automated negotiation systems consisting of self-interested agents, contracts have traditionally been impossible to breach. Such contracts do not allow the agents to efficiently deal with future events. This deficiency can be tackled by using a leveled commitment contracting protocol which allows the agents to decommit from contracts.
Concerning solution quality, the leveled commitment protocols are significantly better than the full commitment protocols of the same type, but the differences between the different leveled commitment protocols are minor
1. How many possible task allocations are there?
2. List each agent's numeric value preference for each of these.
3. Which task allocation will be chosen?
4. List the route of each agent and travel cost incurred?
5. How much of charge does each agent pay/receive?
6. What is the budget balance/deficit?
7. How can some agents beneficially collude by revealing untruthful preferences
8. How can general equilibrium market economy satisfy gross substitutes property?
9. . How much can one agent can gain by acting strategic/speculating instead of acting competitively as price taker?
10. Where would contracts lead to a local optimum when agents use per contract individual rationality as their decision criterion?