Effective Coordination of Multiple Intelligent Agents for Command and Control

1. Effective Coordination of Multiple Intelligent Agents for Command and Control The Robotics Institute Carnegie Mellon University PI: Katia Sycara http://www.cs.cmu.edu/~sycara http://www.cs.cmu.edu/~softagents Key Personnel: Onn Shehory Terry Payne

2. Current Situation • Vast amounts of data from distributed and heterogeneous sources • Uncertain and evolving tactical situation • Shrinking decision cycles • Decision makers distributed in space and time

3. Overall Goal • To develop effective agent-based system technology to support command and control decision making in time stressed and uncertain situations

4. 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

5. 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?

6. Potential Impacts • Reduce time for commanders to arrive at a decision • Allow commanders to consider a broader range of alternatives • Enable commanders to flexibly manage contingencies (replan, repair) • Improve battle field awareness • Enable in-context information filtering

7. Innovative Claims • Scalable, robust and adaptive coordination and control multi-agent strategies • Sophisticated individual agent control • Reusable and customizable agent components • Multi-agent infrastructure coordination tools and environment

8. 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 • multi agent coordination infrastructure consisting of a suite of tools for reliable and low cost building and experimenting with flexible multiagent systems

9. The RETSINAMulti-Agent Architecture User 1 User 2 User u Goal and Task Specifications Results Interface Agent 1 Interface Agent 2 Interface Agent i Tasks Solutions Task Agent 1 Task Agent 2 Task Agent t Info & Service Requests Information Integration Conflict Resolution Replies MiddleAgent 2 Info Agent n Advertisements Info Agent 1 Answers Info Source m Info Source 1 Queries Info Source 2 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.

10. RETSINA Individual Agent Architecture

11. 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

12. 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

13. 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

14. Cooperation - Solutions (continued) • Approach: • Very large systems (millions of agents): • Constant complexity cooperation method • Based on models of multi-particle interaction • Structural organization: • Relation of organization structure and autonomy • Effect on system flexibility, robustness

15. Cooperation - Solutions (continued) • Communication planning: • Change communication patterns to reduce eavesdropping risk • Bundle small message together • Use networks when less congested

16. Competition and Markets • Limited resources result in competition • Market-based approaches: • Assume that agents can find one another • Assume centralized auctioneer • 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

17. 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

18. RETSINA: Testbed for Agent-Based Systems • Continuing development of general purpose multi-agent infrastructure • Agents built from domain-independent, reusable components • Agent behaviors specified in declarative manner • New agent configurations easily built and empirically tested.

19. Coordinating Agents With Human Users Problem: Commanders’ already overloaded For task delegation to be effective, communication with agents should be • natural • flexible: providing planning information when appropriate • concise: providing as little detail as possible • interactive: before and during task execution, agents: • provide explanations of plans • assist users in revising plans during task execution, agents: • report plan’s progress

20. Agent Task Delegation • Languages for task description and delegation • Reconciling human and agent representation of tasks • Structured Natural Language/Graphical task description • Interactive Planning and Execution • user input as constraints on plan formation • execution monitor brings user into loop • Extending RETSINA’s • graphical task editor • planner and execution monitor

21. In-Context Information Management for C2 Agent-Based Information Management • Dependent on • user preferences • decision-making tasks • 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

22. 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

23. 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

24. Research Plan (contd.) • User-Agent Coordination • enhance the functionality of the current agent command language • develop and implement techniques for acquisition and maintenance of user tasks preferences and intentions • develop and implement protocols to enable an agent to accept task-related queries before, during or after task execution and generate natural descriptions of the unfolding execution of its plans • evaluate and refine • Information Management and Decision Support • develop mechanisms for information management (e.g., filtering, integration) in the context of the current problem solving task • develop mechanisms for in-context information monitoring and notification • evaluate and refine

25. 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 • Multi agent coordination infrastructure consisting of a suite of tools for reliable and low cost building and experimenting with flexible multiagent systems

26. Dimensions of Evaluation • Individual Agent eg: • reasoning sophistication • control sophistication • learning capability • degree of self-interestedness • knowledge • data available to the agent • Task eg: • task complexity • task interdependence • task temporal • resource constraints • frequency of task arrival

27. Dimensions of Evaluation (cont) • Environment eg: • number of agents • system load • degree of uncertainty • resource availability • Coordination Mechanism eg: • degree of agent coupling • richness of communication • task delegation mechanism • degree of agent cooperation/competition • Organizational Structure eg: • hierarchy • heterarchy • federation

28. Evaluation Metrics • Individual Agent Performance eg: • accuracy of information returned by an agent • agent service responsiveness • resource consumption • MAS Aggregate Performance eg: • System efficiency • Solution quality • System robustness • System stability • Predictability • Scalability

29. Examples of Experimental Hypothesis

30. Process for Experimentation 1. Formulation of the distributed coordination algorithm 2. Development of experimental infrastructure (eg: simulation tools, making appropriate modifications ro RETSINA components) 3. Running the experiment and collecting statistics 4. Analysis of the results 5. Inter-mechanism evaluation; the results of the simulations of the various mechanisms will be compared to determine performance landscapes of the different coordination mechanisms

31. Inter-Agent World Communications 1. The OAA Facilitator is started, followed by OAA Startit and OAA Monitor. 2. Start the InterOperator. a. We verify its registration as a Retsina agent with the Retsina ANS entry, "OAA_InterOperator". b. We verify its registration as an OAA agent via its registry and advertisement with the OAA Facilitator and by its name, "Retsina_InterOperator", and icon showing in the OAA Monitor. 3. Start the Retsina agent, "KQMLMessageSenderGUI" and register it with theRetsina ANS under the name, "Retsina_Matchmaker". 4. Using OAA Startit, start the other OAA agents. As those agents come onlinethey will register and advertise with the OAA Facilitator. Each registry and advertisement will generate an event which is captured by the InterOperator and forwarded to the "Retsina_Matchmaker". In the future, the real Retsina Matchmaker will be the actual recipient of those messages. 5. Via the "Retsina_Matchmaker", it is possible to send messages to the OAA Facilitator. 6. OAA agents may be disconnected from the OAA Facilitator, or shutdown, and their status change will also be transmitted to the "Retsina_Matchmaker".

32. Inter-Agent World Communications 1. The "OAA Facilitator" is started, followed by "OAA Startit"(cf. <LV>/OAA_Start-It.gif) and "OAA Monitor" (cf. <LV>/OAA_Monitor.gif). 2. Start the InterOperator. a. We verify its registration as a Retsina agent with the Retsina ANS entry,"OAA_InterOperator" (cf. <LV>/TestANS_lookup.gif). b. We verify its registration as an OAA agent via its registry andadvertisement with the OAA Facilitator, Ex. OAA Facilitator> Knowledge source connected: 6 OAA Facilitator> 6 (Retsina_InterOperator) can solve: OAA Facilitator> [update_data(_6771,_6788)] and by its name, "Retsina_InterOperator", and icon showing in the "OAAMonitor" (cf. <LV>/OAA_Monitor_InterOp.gif).

33. Inter-Agent World Communications (cont) 3. Start the Retsina agent, "KQMLMessageSenderGUI" and register it with the"Retsina ANS" under the name, "Retsina_Matchmaker"(cf. <LV>/Test_Retsina_Matchmaker.gif). 4. Using OAA Startit, start the other OAA agents(cf. <LV>/OAA_Start-It_AllAgentsUp.gif, <LV>/OAA_Monitor_AllAgentsUp.gif).As those agents come on-line they will register and advertise with the OAAFacilitator. Each registry and advertisement will generate an event whichis captured by the InterOperator (cf. <LV>/OAA_Monitor_StartOaaWebL.gif) and forwarded to the "Retsina_Matchmaker"(cf. <LV>/Test_Retsina_Matchmaker_Updates.gif). In the future, the realRetsina Matchmaker will be the actual recipient of those messages. 5. Via the "Retsina_Matchmaker", it is possible to send messages to the OAAFacilitator (cf. <LV>/Hypothetical_MsgSend.gif). 6. OAA agents may be disconnected from the OAA Facilitator, or shutdown, andtheir status change will also be transmitted to the "Retsina_Matchmaker"(cf. <LV>/Agent_Shutdown.gif).