Effective Coordination of Multiple Intelligent Agents for Command and Control The Robotics Institute Carnegie Mellon University PI: Katia Sycara Key Personnel: Onn Shehory R. Michael Young

Research Objectives Develop an adaptive, self-organizing collection of intelligent agents that interact with the humans and each other to –integrate information management and decision support –anticipate and satisfy human information processing and problem solving needs –perform real-time synchronization of domain activities –notify users and other each other about significant changes in the environment –adapt to user, task and situation

Technical Challenges What coordination mechanisms are effective for large numbers of sophisticated agents? What are the scaling up properties of these coordination mechanisms? How do they perform with respect to dimensions, such as task complexity, interdependence, agent heterogeneity, solution quality? What guarantees do these mechanisms provide regarding predictability of overall system behavior? Do they mitigate against harmful system behaviors? How to achieve effective human-agent coordination?

The RETSINA Multi-Agent Architecture User 1User 2User u Info Source 1 Info Source 1 Interface Agent 1 Interface Agent 2 Interface Agent i Task Agent 1 Task Agent 2 Task Agent t MiddleAgent 2 Info Agent n Info Source 2 Info Source 2 Info Source m Info Source m Goal and Task Specifications Results SolutionsTasks Info & Service Requests Information Integration Conflict Resolution Replies Advertisements Info Agent 1 Queries Answers distributed adaptive collections of information agents that coordinate to retrieve, filter and fuse information relevant to the user, task and situation, as well as anticipate user's information needs.

Capability-based coordination Open, uncertain environment: –Agents leave and join unpredictably –Agents have heterogeneous capabilities –Replication increases robustness Agent location via Middle agents: –Matchmakers match advertised capabilities –Blackboard agents collect requests –Broker agents process both

Capability-based coordination (cont) Advertisement: –Includes agent capability, cost, etc. –Supports interoperability –Agent interface to the agent society independent of agent internal structure We will test scale-up properties of capability-based coordination

Cooperation Problems with current methods: –Mechanisms not tested in real-world MAS –Simulations?size small (~20 agents) –Complex mechanism do not scale up We will provide algorithms for efficient group formation

Cooperation - solutions (continued) Approach:  Very large systems (millions of agents): –Constant complexity cooperation method –Based on models of multi-particle interaction  Structural organization: –Trade-off between reduced complexity and loss of autonomy –Effect on system flexibility, robustness

Competition and Markets Limited resources result in competition Market-based approaches: –Assume that agent can find one another – Otherwise, convergence results do not hold Approach:  Utilize financial option pricing: Prioritize tasks by dynamic valuation Allows flexible contingent contracting Analysis of large MAS via economics methods

Competition and Markets (contd)  Combine our capability-based coordination with market mechanisms  Mechanism design: –Design enforceable mechanisms for self-interested agents –Resolve Tragedy of Commons by pricing schemes. –Devise mechanisms to motivate truthful behavior

Coordinating Agents With Human Users Agent Task Delegation –Languages for task description and delegation –Interactive planning and execution Extending RETSINA’s graphical task editor In-Context Information Management for C2 –Dependent upon user preferences, task context and evolving situation –Agents’ responsibilities Represent users’ task environment Monitor significant changes Provide appropriate notification to user or responsible agent Learn to track and anticipate user’s information needs Learn appropriate times and methods for presenting information

Research Plan Agent Control –mapping of task model and requirements to the appropriate coordination strategy –mapping of constraints of the environment, other agents and available resources to appropriate coordination strategy –experimental evaluation, analysis and refinement Agent Coordination –design/refine coordination algorithm –implement appropriate experimental infrastructure –implement the coordination strategy and evaluate along different dimensions –analyze the results and refine algorithm design and experimental process

Additional Slides

Major Project Deliverables Prototype multiagent system that aids human military planners to perform effective “in context” information gathering, execution monitoring, and problem solving Reusable “agent shell” that includes domain independent components for representing and controlling agent functionality, so that agents can be easily produced for different types of tasks Effective multiagent coordination protocols, that are scalable, efficient and adaptive to user task and planning context <ulti agent coordination infrastructure consisting of a suite of tools for reliable and low cost building and experimenting with flexible multiagent systems

Agent Coordination in RETSINA Build information management agents for C2 based on RETSINA mechanisms for agent coordination Goal and task structures provide user and agent context Information agents form and execute plans that –involve queries for future information monitoring –take situational constraints into account –work around notification deadlines Build upon existing base of information management agents