1. Agent Communication in Multi Agent Systems

2. Reference Weiss – Chapter 2 Wooldridge – Chapter 8

3. Multiple agent systems There are cases of single independent agents Multiple agents are more typical –Networks and interconnected systems This discussion involves analysis and design of multi agent environments –Environment provide computational infrastructure Protocols for agents to communicate Protocols for agents to interact

4. Example of Comm Protocol Example of a comm protocol –Propose a course of action –Accept a course of action –Reject a course of action –Retract a course of action –Disagree with proposed course of action –Counter propose course of action Interaction between A1 and A2 –A1 proposes a course of action to A2 –A2 evaluates the proposal and does one of the following Sends acceptance to A1 Sends counter proposal to A1 Sends disagreement to A1 Sends rejection to A1

5. Lost update problem Process synchronization is critical –Shared variable v, is read by p1 and processed. –Process p2 updates v. –Process p1 updates v. –p2 updates are lost –Serialization

6. Communication: Object vs. Agent OO: Communication as Method Invocation –Two objects O1 and O2. –Object O1 has a public method m1. –Object O2 communicates with O1 by invoking m1, i.e. O2 executes O1. m1(arg); arg is argument communicated by O2 2 to O1. –In effect O2 controls execution of O1. In agent environment the agents are autonomous and hence this is not acceptable. –Agents communicate to influence the other agent.

7. Why Multi Agent Systems? Info is geographically distributed Info arch is large and complex –Many components, concepts, large data volume, complex interconnections Multi modal info –Text, database, voice, images, video… –Static, dynamic, probabilistic

8. How to deal with complexity? Potential ways to deal with complexity –Modularity, distribution, abstraction, intelligent info processing (find and modify)  DAI / Agents –Develop agents independently, make agents autonomous Computational agents are distributed –Application programs, –Active info resources – available globally, –Wrappers for legacy systems, –On-line services

9. Properties of Multi Agent Env Infrastructure including comm and interaction protocols No centralized controller (designer) Support autonomy Agents may cooperate or compete

10. Properties of MAS PropertiesRange of values Design AutonomyPlatform, interaction protocol, language, internal arch Communication Infrastructure Blackboard (shared mem) or Message passing; Synch or Asynch; Push or Pull; Point-to-point, Multicast, or Broadcast Directory servicesYellow or white pages Message ProtocolKQML, CORBA, HTTP, HTML SecurityTimestamp / Authentication Operations SupportArchiving / Redundancy / Restoration / Accounting From Table 2.1 in [1]

11. Communications Relies on Syntax (structure of comm – at the symbol level) Semantics (what the symbols denote) Pragmatics (interpretation of the symbols) Semantics + Pragmatics leads to understanding of the meaning of the communication Agents must understand and be understood

12. Dimensions of Meaning Descriptive vs Prescriptive –Describe phenomena vs prescribe behavior –Descriptions are good for humans, what about agents? –ACL are designed to communicate behavior and activities Personal vs Conventional meaning –Agents should use conventional (standards). But what about context? Semantics vs Pragmatics –Pragmatics specifies how communication is used. Environment. Context Contextuality –Context of the present state of the communicator. Environment and history of actions. Communicating agent. Identity of the communicator. Coverage – smaller languages are better suited for agent communication Cardinality: single message interpreted differently than a broadcast message.

13. Agent Communication Coordination requires the agent to predict behavior of the other agents in the system – requires good models Coord – Competition – Negotiation Cooperation – Planning – Centralized Plnng Distributed Plnng Coherence – achieve global coherence without centralized control –Economic markets are good at determining price, but not necessarily provides optimal resource allocation from Herb Simon

14. Message Types Agents must be capable to participate in a dialog –Potential agent role Active, passive or both Master, slave or peer –Passive agents Accept info (assertions), Accept query and send a reply (assertion) From the comm network there is no difference between an unsolicited assertion or an assertion in reply to a query –Active agents Issue queries, issue assertions Control subagents, Monitor environment Speech act theory is the basis of the inter agent communication –Views natural language as actions – requests, suggestions, commitments, and replies. –Locution – the spoken (physical) utterances –Illocution – intended meaning of the spoken utterance –Perlocution – action resulting from the locution

15. Agent Capabilities Basic Agent Passive Agent Active Agent Peer Agent Receives assertions  Receives queries  Sends assertions  Sends queries  Table 2.3 from [1]

16. Inter Agent Message Types Communicative ActionIllocutionary ForceExpected Result AssertionInformAcceptance QueryQuestionReply InformAcceptance Request ExplanationInformAgreement Command PermissionInformAgreement RefusalInformAgreement Offer/BidInformAgreement Acceptance Agreement ProposalInformOffer/Bid Confirmation Retraction Denial Table 2.4 from [1]

17. Communication Protocol Typical levels –Lowest level: interconnection –Middle level: format, syntax of the info being transferred –Top level: meaning or semantics of the info Binary: one sender, one receiver N-ary: Broadcast, Multicast – one sender, N receivers Data structure –Sender –Receiver (s) –Languages in the protocol –Encoding and decoding functions –Actions to be taken by the receiver

18. Knowledge Query and Manipulation Language (KQML) Protocol for info and knowledge exchange Structure (KQML-performative :sender :receiver :language :ontology :content ……) Keywords (preceded by :) can be in any order. Others –reply-with, in-reply-to Message can be understood by agents – assuming language and ontology knowledge

19. KQML: Inter Agent & Agent Program Communication Agent Application Program KQML

20. KQML – Modes of Communication Client Server Client Server Client Server query reply Synch: Blocking query – wait for reply query reply next reply handle subscribe reply Server maintains state: individual replies on request Asynch: nonblocking subscribe results in replies

21. Knowledge Interchange Format Formal syntax for knowledge representation Example: Block A on Block B. (tell :senderAgent1 :receiverAgent2 :languageKIF :ontologyBlocks-World :content(AND (Block A) (Block B) (On A B)))

22. Nested KQML (forward :fromAgent1 :toAgent2 :senderAgent1 :receiverAgent3 :languageKQML :ontologyKQML-ontology :content(tell :sender Agent1 :receiver Agent2 :language KIF :ontology Blocks-World :content (AND (Block A) (Block B) (On A B)))) Forward:from = Content:sender = Agent1 Forward:to = Content:receiver = Agent2 Forward:receiver = Agent3

23. KQML performatives Basic query (evaluate, ask-one, ask-all,…) Multiresponse query (stream-in, stream-all,..) Response (reply, sorry,…) Generic info (tell, achieve, cancel, untell, unachieve, …) Generator (standby, ready, next, rest,…) Capability-definition (advertise, subscribe, monitor,…) Networking (register, unregister, forward, broadcast,…)

24. Knowledge Interchange Format (KIF) Proposed standard for intelligent agents, expert systems, databases… Examples (salary xxx-yy-zzzz designer 40000) (> (* (width chip1) (length chip1)) (* (width chip2) (length chip2)))

25. Example KQML dialog (evaluate :senderA:receiverB :languageKIF:ontology motors :reply-withq1:content(val(torque m1))) (reply :senderB:receiverA :languageKIF:ontology motors :in-reply-toq1:content(= (torque m1) (scalar 12 kgf))) Note: q1 is query reference number From Figure 8.2 in [2]

26. Example KQML dialog (stream-about :senderA:receiverB :languageKIF:ontology motors :reply-withq1:content(val(torque m1))) (tell :senderB:receiverA :in-reply-toq1:content(= (torque m1) (scalar 12 kgf))) (tell :senderB:receiverA : in-reply-to q1:content(= (status m1) normal)) (eos :senderB:receiverA : in-reply-to q1) stream-about: S wants all relevant answers in R’s Virtual Knowledge Base. Output is streamed. eos ends the stream From Figure 8.2 in [2]

27. FIPA ACL (inform :senderagent1 :receiveragent2 :content(pricegood2150) :languages1 :ontologyhpl-auction ) FIPA=Foundation of Intelligent Physical Agents Message structure is similar to KQML Message attribute fields is also similar to KQML FIPA performatives are different than KQML –20 performatives –Inform tells the receiver to believe the message content; implies that sender also believes the message

28. Ontologies Specification of the objects, concepts and relationships –In the Block World example, BLOCK represents a concept and ON is a relationship Each agent must represent its knowledge using the vocabulary of a specific ontology Note KQML – specifies the Ontology How are Ontologies co-ordinated?

29. Ontologies for ACL Represent part of the world. Shared virtual world, which provide the terms for communication. If 2 agents agree on the upper nodes of a taxonomy, then these agents can focus on the language content in this context.

30. Agent Interaction Protocols Interaction implies conversation Objective: –Ensure achievement of overall goals, –Coherence across agents –Retain agent autonomy Shared goals, common tasks, reduce conflicts, pool knowledge and evidence

31. Agent Interaction Protocols (contd) Co-ordination Co-operation Contract Net Blackboard Negotiation AIGA

32. Co-ordination Protocols Reason for co-ordination: better utilization of resources, avoid duplication, maintain coherence –Timely updates, agent synchronization –Distribute control and data Disadv: System state is distributed Adv: Reduce points of bottleneck –Goal graph Relates goals and resources Identifies dependencies Assignment of goals to agents Manage graph traversal and report results

33. Co-operation Protocol Supports decomposition and distribution of tasks (divided and conquer) –Avoid overloading of critical resources –Task assigned to agents with matching skills –Master / slave relationships in task assignment –Minimize communication and synchronization cost: spatial and semantic proximity –Migrate tasks if necessary –Redundancy and fault tolerance Task distribution approaches –Market mechanisms: price, utility –Contract net –Credibility, belief management –Static plans: task to resource map

34. Task Assignment Spatial Functional Agent 1 Agent 3 Agent 2 Neurologist PedatricianCardiologist Radiologist Internist

35. Contract Net Manager wants to find contractors –Announce task (RFP) –Receive bids –Award contracts –Results, task accomplishment Contractors role –Get RFP –Evaluate capability –Respond (Bid, No Bid) –Perform task –Report results Manager assess contractor capability Distributed computing implications

36. Structure Contract Net Structure the bid –Addressee –Capability requirements –Task abstraction –Bid spec –Expiration Contractor response –Capability –Capacity –Pricing Multi level interaction Contractor capability and capacity assessment Manager as a prime contractor Negotiation Synchronization

37. Blackboard Specialized Knowledge Sources (KS) Multiple approaches to problem solution Knowledge representation is KS controlled Control transfer Data exchange Synchronization

38. Blackboard Architecture Executing Activated KS Control Components Library Of KSs Blackboard Events Pending KS Activations Distributed Memory Systems? Granuality?

39. Negotiation Goes through a cycle –Offer –Evaluation –Identify agreements –Identify disagreements –Repeat with counter offer Granularity, efficiency, stability