1 / 10

An Infrastructure for Adaptive Control of Multi-Agent Systems

An Infrastructure for Adaptive Control of Multi-Agent Systems Dr. Karl Kleinmann, Richard Lazarus, Ray Tomlinson KIMAS, October 1, 2003 karl.kleinmann@bbn.com. Control of DMAS: Problem Description.

gomer
Download Presentation

An Infrastructure for Adaptive Control of Multi-Agent Systems

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. 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”

  3. Cougaar Project Background and Control Objectives HW Failures SW Failures UltraLogDMAS Logistic Actions (Plan) Logistics Requirements MilitaryLogisticsOperation • 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

  4. Cougaar Architecture Overview TRANSCOM 1BDE 2BDE • 100% Java Architecture for building large DMAS • Proven Scalability • Prototype with 500 distinct agents distributed over 5LAN network of >100 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

  5. Control Levels designed to achieve Control Objectives HW Failures SW Failures UltraLogDMAS AgentController UltraLog DMAS TRANSCOM Logistic Actions (Plan) Logistics Requirements 1BDE 2BDE Agent Components MilitaryLogisticsOperation • 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)

  6. The Cougaar Control Infrastructure

  7. Example (Part of the Open Source Code Base) AdaptivityEngine Provider Agent Consumer Agent Allocation Alg. Task AllocatorPlugin CPU Task Rate • 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)

  8. 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 (-> 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:

  9. 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

  10. For more information … • BBN Technologies: http://www.bbn.com • Cougaar: http://www.cougaar.org • UltraLog: http://www.ultralog.net • Other Cougaar-related KIMAS’03 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

More Related