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

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

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

Research Approach 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 about significant changes in the environment –adapt to user, task and situation

Research Issues 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?

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

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

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

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.

RETSINA Individual Agent Architecture

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

Cooperation - solutions (continued)  Communication planning: –Change communication patterns to reduce eavesdropping risk –Bundle small message together –Use networks when less congested

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 揟 ragedy of Commons?by pricing schemes. –Devise mechanisms to motivate truthful behavior

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.

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

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

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

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

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

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