Agent Communication in Multi Agent Systems

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Agent Communication in Multi Agent Systems
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Reference

• Weiss Chapter 2
• Wooldridge Chapter 8

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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

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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

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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

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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.

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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

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How to deal with complexity?

• Potential ways to deal with complexity
• Modularity, distribution, abstraction,
  intelligent info processing (find and modify) Þ
  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

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Properties of Multi Agent Env

• Infrastructure including comm and interaction
  protocols
• No centralized controller (designer)
• Support autonomy
• Agents may cooperate or compete

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Properties of MAS
Properties Range of values
Design Autonomy Platform, 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 services Yellow or white pages
Message Protocol KQML, CORBA, HTTP, HTML
Security Timestamp / Authentication
Operations Support Archiving / Redundancy / Restoration / Accounting
From Table 2.1 in 1
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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

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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.

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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

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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

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Agent Capabilities
Basic Agent Passive Agent Active Agent Peer Agent
Receives assertions
Receives queries
Sends assertions
Sends queries
Table 2.3 from 1
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Inter Agent Message Types
Communicative Action Illocutionary Force Expected Result
Assertion Inform Acceptance
Query Question Reply
Reply Inform Acceptance
Request Request
Explanation Inform Agreement
Command
Permission Inform Agreement
Refusal Inform Agreement
Offer/Bid Inform Agreement
Acceptance
Agreement
Proposal Inform Offer/Bid
Confirmation
Retraction
Denial
Table 2.4 from 1
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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

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Knowledge Query and Manipulation Language (KQML)

• Protocol for info and knowledge exchange
• Structure
• (KQML-performative
• sender ltwordgt
• receiver ltwordgt
• language ltwordgt
• ontology ltwordgt
• content ltexpressiongt
• )
• 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

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KQML Inter Agent Agent Program Communication
KQML
KQML
Application Program
Agent
Agent
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KQML Modes of Communication
Server
Client
query
reply
Synch Blocking query wait for reply
query
Client
Server
handle
next
reply
next
reply
Server maintains state individual replies on
request
subscribe
Server
Client
reply
reply
reply
Asynch nonblocking subscribe results in replies
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Knowledge Interchange Format

• Formal syntax for knowledge representation
• Example Block A on Block B.
• (tell
• sender Agent1
• receiver Agent2
• language KIF
• ontology Blocks-World
• content (AND (Block A) (Block B) (On A B)))

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Nested KQML

• (forward
• from Agent1
• to Agent2
• sender Agent1
• receiver Agent3
• language KQML
• ontology KQML-ontology
• content (tell
• sender Agent1
• receiver Agent2
• language KIF
• ontology Blocks-World
• content (AND (Block A) (Block B) (On A B))))
  

Forwardfrom Contentsender Agent1 Forwardto
Contentreceiver Agent2 Forwardreceiver
Agent3
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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,)

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Knowledge Interchange Format (KIF)

• Proposed standard for intelligent agents, expert
  systems, databases
• Examples
• (salary xxx-yy-zzzz designer 40000)
• (gt ( (width chip1) (length chip1))
• ( (width chip2) (length chip2)))

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Example KQML dialog

• (evaluate
• sender A receiver B
• language KIF ontology motors
• reply-with q1 content (val (torque m1)))
• (reply
• sender B receiver A
• language KIF ontology motors
• in-reply-to q1 content ( (torque m1) (scalar
  12 kgf)))
• Note q1 is query reference number

From Figure 8.2 in 2
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Example KQML dialog

• (stream-about
• sender A receiver B
• language KIF ontology motors
• reply-with q1 content (val (torque m1)))
• (tell
• sender B receiver A
• in-reply-to q1 content ( (torque m1) (scalar
  12 kgf)))
• (tell
• sender B receiver A
• in-reply-to q1 content ( (status m1)
  normal))
• (eos
• sender B receiver A
• in-reply-to q1)
• stream-about S wants all relevant answers in
  Rs Virtual Knowledge Base. Output is streamed.
  
• eos ends the stream

From Figure 8.2 in 2
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FIPA ACL

• (inform
• sender agent1
• receiver agent2
• content (price good2 150)
• language s1
• ontology hpl-auction
• )
• FIPAFoundation 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

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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?

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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.

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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

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Agent Interaction Protocols (contd)

• Co-ordination
• Co-operation
• Contract Net
• Blackboard
• Negotiation
• AIGA

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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

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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

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Task Assignment
Agent 2
Agent 1

• Spatial
• Functional

Agent 3
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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

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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

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Blackboard

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

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Blackboard Architecture
Distributed Memory Systems? Granuality?
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Negotiation

• Goes through a cycle
• Offer
• Evaluation
• Identify agreements
• Identify disagreements
• Repeat with counter offer
• Granularity, efficiency, stability

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