Coordination Artifacts in Multi-Agent Systems April 19, 2005 IEEE KIMAS 2005 Sarah Siracuse, John Zinky, Richard Shapiro

2Agenda Motivation for Coordination Artifacts in MAS Coordination Artifacts: Designs & Benefits Separation of Function: Coordination logic vs. Domain logic Implementation of Coordination Artifacts using Cougaar Works well in Tightly-coupled Systems Performance Analysis: QoS Opportunity Conclusions

3 Motivation for Coordination Artifacts in MAS Coordination observations –MAS application Cougaar agent architecture ~1000+ agents on ~100 hosts –Many different kinds of implicit coordination in heterogeneous systems –Coordination implementation Mixed in with domain logic Spans lots of places in the code Coordination Artifact –Separates coordination implementation from domain logic –Distinguishes between various kinds of coordinations –Has state Controller Manager Peer Sensor Translate Collect Disseminate Synchronize Aggregate Summarize Typical Agent Control Society

4 CAs Separate Coordination Activity from Domain Processing Objective Coordination (Outside Agent) –Coordination encapsulated Outside domain logic –Environment-based –Mediated communication –e.g. Ant trails Subjective Coordination (Inside Agent) –Coordination mixed in with domain logic –Dialog-based –Direct Messaging –e.g. TCP/IP, Instant Messaging,FIPA Agent Communication Language Agent CAs

5 Agent CAs are First Class Entities that coordinate Interaction between Agents Coordination Artifact (CA) Agent Defines roles Agent Coordination Artifacts: CAs –Are first-class entities in MAS –Define explicit roles for role-players –Offer shared state between the role-player & the CA –Coordinate behavior among role-players –Have distributed implementation Role-players Shared state

6 Server CA Client CA Sensor Coordination Inter-Agent Coordination CAs Unify Agent-to-Agent and Agent-to-Environment Communication Agent Physical Environment Non-Agent Systems Other Agents Persisted Storage

7Agenda Motivation for Coordination Artifacts in MAS Coordination Artifacts: Designs & Benefits Separation of Function: Coordination logic vs. Domain logic Implementation of Coordination Artifacts using Cougaar Works well in Tightly-coupled Systems Performance Analysis: QoS Opportunity Conclusions

8 Agent Cougaar Components Implement Ad-hoc Coordination Agent Domain Logic Sensor Plugin Sensor Comp Agent Blackboard Inter-agent Messaging Components Net Client Libraries Server Libraries Client Plugin Server Plugin Physical Environment Comm Plugin Non-Agent Systems Remote Agents

9 Agent B Agent A Rule Engine Fact Base Fact Receptacle Facet Black- board Relay Logic Provider Message Transport Rule Engine Fact Base Fact Receptacle Facet Black- board Relay Logic Provider Message Transport RMI Host A Host B Distributed Coordination Artifacts Layered Over Cougaar Components CA

10 Coordination Artifacts work best in Tightly-Coupled Systems Tightly coupled (Ideal CA applications): Long Term Relationships Group relationships Push meta-data in anticipation of need E.g. Cougaar with Coordination Artifacts Loosely coupled (Bad fit for CAs): Transient Relationships Pair relationships Pull meta-data when needed E.g. Web-Services Controller Manager Peer Sensor Translate Collect Disseminate Synchronize Aggregate Summarize Typical Agent Control Society

11 Coordination Performance Depends on Underlying Topology and Resources Coordination Task Tick Sync Coordination M SSSS …

12 Coordination Performance Depends on Underlying Topology and Resources Coordination Task Tick Sync Coordination M SSSS … Distant master (31 hosts) Distant slave (31 hosts) Dual processors Single processor 2.8GHz Single Processor Dual Procesor Dual Processors M WAN S Resources & Roles

13 Coordination Performance Depends on Underlying Topology and Resources Coordination Task Tick Sync Coordination M SSSS … Distant master (31 hosts) Distant slave (31 hosts) Dual processors Single processor 2.8GHz Single Processor Dual Procesor Dual Processors M WAN S Resources & Roles FlatTreeChain Topology M TT SSSS M SSSSSS S TT T TT M Flat Tree Chain

14 Coordination Performance Depends on Underlying Topology and Resources Tick Sync Coordination S Coordination Task M SSS … Distant master (31 hosts) Distant slave (31 hosts) Dual processors Single processor 2.8GHz Single Processor Dual Procesor Dual Processors M WAN S Resources & Roles Performance (ticks/second) FlatTreeChain Topology M TT SSSS M SSSSSS S TT T TT M Flat Tree Chain

15 Coordination Performance Depends on Underlying Topology and Resources Coordination Task Tick Sync Coordination M SSSS … Distant master (31 hosts) Distant slave (31 hosts) Dual processors Single processor 2.8GHz Single Processor Dual Procesor Dual Processors M WAN S Resources & Roles Performance (ticks/second) FlatTreeChain Topology M TT SSSS M SSSSSS S TT T TT M Flat Tree Chain

16 CAs can dynamically change the topology as the network and/or the roles change. QoS Adaptation via CAs Distant master (31 hosts) Distant slave (31 hosts) Dual processors Single processor 2.8GHz Single Processor Dual Procesor Dual Processors M WAN S Resources & Roles Performance (ticks/second) FlatTreeChain Topology M TT SSSS M SSSSSS S TT T TT M Flat Tree Chain

17Conclusions CAs present a formal model for coordinated communication –Blackboard-based, not FIPA message-based Ease use of blackboard-based MAS –Unify Blackboard interfaces, including Web Services –Correlate multiple changes to blackboard objects –Partition the blackboard for domain and system reasons Separation of Coordination and Domain processing –Make the intermediary a first-class entity Place to add QoS-adaptation Future Work –Might facilitate reuse or composability of coordinations –Might examine them in off-line analysis –Might support code generation