1 kimas 2003dr. k. kleinmann an infrastructure for adaptive control of multi-agent systems dr. karl...

1KIMAS 2003Dr. K. Kleinmann

An Infrastructure for Adaptive Control of Multi-Agent Systems

Dr. Karl Kleinmann, Richard Lazarus, Ray Tomlinson

KIMAS, October 1, 2003

[email protected]


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”


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 Infrastructure

despite Loss of Resources• Security Function

– Maintain System Integrity despite Information Attacks

MilitaryLogisticsOperation

UltraLogDMAS

Logistics Requirements

Logistic Actions (Plan)

SW Failures HW Failures


Cougaar Architecture Overview

• 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

PLATFORM OS

JAVA VMJAVA VM

COUGAAR NODE

Agent Binder

Agent Binder

PLATFORM SERVICES

COUGAAR NODE SERVICES

Agent Binder

AGENT.AGENTFRAMEWORKSVCS.

PluginBinder

PluginBinder

PluginBinder

PluginBinder

PlugInPlugIn

COUGAAR NODE

Agent Binder

COUGAAR NODE SERVICES

Agent BinderAgent Binder


PluginBinder

PluginBinder

PluginBinder

PluginBinder

Agent Binder


PluginBinder

PluginBinder

PluginBinder

PluginBinder

PlugInPlugIn

PlugInPlugIn

PlugIn

PlugIn

TRANSCOM

1BDE

2BDE


Control Levels designed to achieve Control Objectives

• Agent Infrastructure-Level Control– Parameters of Components within an

Agent– “Local Agent Autonomy” (e.g., Message

Compression; Status Report Rate)

MilitaryLogisticsOperation

UltraLogDMAS

Logistics Requirements

Logistic Actions (Plan)

SW Failures HW Failures

Agent Components

AgentController

TRANSCOM

1BDE2BDE

UltraLog DMAS

• Application-Level Control– Complex Actions or Sequences of Actions

composed of Control Primitives– “Specific Defenses against Stresses”

(e.g., Load Balancing; Agent Restart)


The Cougaar Control Infrastructure

ProcessingComponents

(Plugins)

Sensors

Publish real-time sensor conditions, e.g., Load, Resource AvailabilityTHREATCON

Sets Operating Modes for Components based on plays in Playbook and current Sensor Conditions

Read Playbook

Constrain Playbook based on OperatingMode policy direction

Operating mode policy manager reads Operating mode policies (relayed from other agents) from blackboard

Publish OperatingModes and TechSpecsGet Condition

by name

Get OperatingModeby name

Blackboard

RelayLP

Other agents

Inter-AgentOperatingMode Policies

Operating ModePolicy Manager

Adaptivity Engine

OperatingModeService

ConditionService

Playbook

PlaybookConstrain Service

PlaybookRead

Service

Playbook Manager


(Plugins)


(Plugins)

SensorsSensors

Publish real-time sensor conditions, e.g., Load, Resource AvailabilityTHREATCON

Sets Operating Modes for Components based on plays in Playbook and current Sensor Conditions

Read Playbook

Constrain Playbook based on OperatingMode policy direction

Operating mode policy manager reads Operating mode policies (relayed from other agents) from blackboard

Publish OperatingModes and TechSpecsGet Condition

by name

Get OperatingModeby name

Blackboard

RelayLP

Blackboard

RelayLPRelayLP

Other agents

Inter-AgentOperatingMode Policies

Operating ModePolicy Manager

Adaptivity Engine

OperatingModeService

ConditionService

Playbook


PlaybookRead

Service

Playbook Manager

Playbook


PlaybookRead

Service

Playbook Manager


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)

Task AllocatorPlugin

AdaptivityEngine

Task Rate CPU

Allocation Alg.

Prov

ider

Age

nt

Cons

umer

Age

nt

Sensor Values (Conditions) over time

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.0

1 5 9 13 17 21 25 29 33 37 41

Task Rate(Tasks/sec)

Available CPU (%/10)

Control Input (Operating Mode) over time

0

100

200

300

400

500

600

1 5 9 13 17 21 25 29 33 37 41

Iterations (cycl/sec)


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:

AE Playbook (Control Algorithm)

0

200

400

600

1 51 101 151 201

Condition (Task Rate / CPU )

Op

erat

ing

Mo

de

(Allo

cati

on

It

erat

ion

s)Avail. CPU Allocations

high max med

low med min

low high Task Rate


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


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

1 kimas 2003dr. k. kleinmann an infrastructure for adaptive control of multi-agent systems dr. karl...

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