An Infrastructure for Adaptive Control of MultiAgent Systems

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• An Infrastructure for Adaptive Control of
  Multi-Agent Systems
• Dr. Karl Kleinmann, Richard Lazarus, Ray
  Tomlinson
• KIMAS, October 1, 2003
• karl.kleinmann_at_bbn.com

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Control of DMAS Problem Description

• Characteristics of Distributed Multi-Agent
  Systems (DMAS) reason that formal methods of
  control theory are rarely applied in software
  engineering for agent systems
• What makes DMAS a hard (complex) control
  problem?
• Dynamic system boundaries, interactions and
  communication paths
• System size, number of internal states, degrees
  of freedom (flexibility)
• Strong couplings between system states (shared
  resources)
• What makes DMAS a unique control problem?
• Shape of cost functions and performance criteria
• Changes of control inputs at (almost) no cost
  (nonlinear impact)
• Explicit model available, accurate description of
  behavior (code)
• System as its own simulator
• Extensive experimentation for free (automated
  testing)
• Control approaches and parameter variations by
  trial and error

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Cougaar Project Background and Control Objectives

• Paper presents the Control Infrastructure of the
  Cougaar Distributed Agent System
• Cougaar is an Open Source Agent Infrastructure
  developed under the DARPA Programs
• ALP (1996-2001) Military Logistics Planning
• UltraLog (2001-2004) Survivable Logistics
  Planning and Execution
• Primary System Function
• Logistics Plan
• Robustness Function
• Maintain Processing Infrastructuredespite Loss
  of Resources
• Security Function
• Maintain System Integrity despite Information
  Attacks

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Cougaar Architecture Overview

• 100 Java Architecture for building large DMAS
• Proven Scalability
• Prototype with 500 distinct agents distributed
  over 5LAN network of gt100 machines
• Two-Level interaction model
• Intra-agent blackboard for tightly-coupled
  interactions
• Inter-agent message passing for scalable
  loosely-coupled interactions
• Distributed object management
• Prototype/delegation data model
• Capabilities-based representations
• Two-Dimensional containment model
• Components can be both containers and plugins

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Control Levels designed to achieve Control
Objectives

• Application-Level Control
• Complex Actions or Sequences of Actions composed
  of Control Primitives
• Specific Defenses against Stresses (e.g., Load
  Balancing Agent Restart)

• Agent Infrastructure-Level Control
• Parameters of Components within an Agent
• Local Agent Autonomy (e.g., Message
  Compression Status Report Rate)

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The Cougaar Control Infrastructure
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Example (Part of the Open Source Code Base)

• 2 Agents
• (Consumer) Provider
• 2 Sensor Inputs (Conditions)
• Task Rate Avail. CPU
• 1 Control Input (Operating Mode)
• Task Allocator Plugin Mode(Tradeoff Accuracy vs
  Speed)

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Interpretation of the Approach

• Infrastructure allows both feedforward and
  feedback control, depending on selection of
  Conditions and Operating Modes
• Heuristic parametrization of Plays makes
  Adaptivity Engine typically nonlinear controller
  (-gt fuzzy control)
• If Plays in Adaptivity Engine are modified
  according to TechSpecs or constrained by
  Policies, Control System becomes truly
  Adaptive
• Rule Matrix in Example

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Conclusions

• Presented Agent-level Control Infrastructure for
  the Open Source Cougaar Architecture
• Generic Approach allows to address Control
  Objectives of a DMAS Survivability Application
• Connects Software Engineering with Control Theory
  Models in Order to leverage Control Theory
  Methodologies
• Future Research Issues
• Components publishing TechSpecs
• Deconfliction of Application-level Control
  Strategies

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For more information

• BBN Technologies http//www.bbn.com
• Cougaar http//www.cougaar.org
• UltraLog http//www.ultralog.net
• Other Cougaar-related KIMAS03 papers
• Multi-Tier Communication Abstractions for
  Distributed Multi-Agent Systems, M. Thome, et al
• Multi-resolutional Knowledge Representation for
  Logistics Systems using Prototypes,Properties and
  Behaviors, J. Berliner, et al