Effective Coordination of Multiple Intelligent Agents for Command and Control

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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
• http//www.cs.cmu.edu/softagents

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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 each other about significant
  changes in the environment
• adapt to user, task and situation

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Technical Challenges

• What coordination mechanisms are effective for
  large numbers of sophisticated agents?
• What are the scaling-up properties of these
  mechanisms?
• How do they perform with respect to dimensions
  such as
• task complexity and interdependency
• 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?

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The RETSINA Multi-Agent Architecture
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.
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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

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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

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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

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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

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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

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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

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Coordinating Agents With Human Users

• User-to-Agent task delegation
• Languages for task description and delegation
• Interactive planning and execution
• 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 users information
  needs
• Learn appropriate times and methods for
  presenting information

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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 algorithms
• implement appropriate experimental infrastructure
• implement the coordination strategy and evaluate
  along different dimensions
• analyze the results and refine algorithm design
  and experimental process

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Additional Slides
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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
• ltulti agent coordination infrastructure
  consisting of a suite of tools for reliable and
  low cost building and experimenting with flexible
  multiagent systems

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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