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Outline
• Definition
• Issues and elements of MAS– MAS architectures– Coordination– Collaboration– Several issues in designing competitive MAS
• Applications
• MAS research direction
• Summary
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Multi-agent Systems
• A multi-agent system contains a number of agents…– …which interact through communication…– …are able to act in an environment…– …have different “spheres of influence” (which may coincide)…– …will be linked by other (organizational) relationships
• MAS as seen from distributed AI– A loosely coupled network of entities that work to-
gether to find answers to problems that are beyond the individual capabilities or knowledge of each en-tity
• A more general meaning– systems composed of autonomous components
that exhibit the following characteristics:• each agent has incomplete capabilities to solve a
problem• there is no global system control• data is decentralized• computation is asynchronous
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Overview of MAS
• Aspects of multi-agent systems– Cooperative vs. competitive– Homogeneous vs. heterogeneous– Macro vs. micro– Interaction protocols and languages– Organizational structure– Mechanism design / market economics– Learning
• Types of MAS– Cooperative MAS
• Distributed problem solving: Less autonomy• Distributed planning: Models for cooperation and teamwork• Typical (cooperative) MAS domains
– Distributed sensor network establishment– Distributed vehicle monitoring– Distributed delivery
– Competitive or self-interested MAS• Distributed rationality: Voting, auctions• Negotiation: Contract nets
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Comparison with Traditional Approaches
• Traditional– Client-server– Low-level messages– Synchronous– Can not do the job!
• Agent break-throughs
– Peer-to-peer topology– Blackboard coordina-
tion model– Encapsulated mes-
saging– High-level message
protocols
A14<MAS>-5
Client Server
IntelligentAgents
IntelligentAgents
IntelligentAgents
Function(Parameters)
Return(Parameters)
Blackboard
MessageReply
Traditional Software
Agents IntelligentAgentsIntelligent
Agents
IntelligentAgentsIntelligent
Agents
IntelligentAgentsIntelligent
Agents
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Main Points in MAS
• MAS researchers develop communications languages, interaction protocols, and agent architectures that facilitate the development of multi-agent systems
• MAS researcher can tell you how to program each ant in a colony in order to get them all to bring food to the nest in the most efficient manner, or how to set up rules so that a group of selfish agents will work together to accomplish a given task
• MAS researchers draw on ideas from many disciplines outside of AI, including biology, sociology, economics, organization and manage-ment science, complex systems, and philosophy
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Key Elements of MAS
• A coordination mechanism supported by a common agent communi-cation language and protocol
• A collaboration mechanism supported by agent community architec-ture (including agent and interaction architecture) to support the or-ganization goal
• A shared ontology
• Popular MAS architectures– Object Manager Group (OMG)– Foundation for Intelligent Physical Agents (FIPA)– Knowledgeable Agent-oriented System (KAoS)– Open Agent Architecture (OAA)– General Magic group
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MAS Architectures (1)
• OMG’s Model– Composed of agents and agencies that collaborate using general patterns and
policies– Agents are characterized by: capabilities, type of interaction and mobility– Agencies support:
• concurrent execution of agents• security• agent mobility
• FIPA’s Model – Agents– Agent Platform (AP)– Directory Facilitator (DF)– Agent Management System (AMS)– Agent Communication Channel (ACC)– Agent Communication Language (ACL)
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MAS Architectures (2)
• KAoS’s Model– An Open Distributed Architecture for Software agents– Defines various agent implementations– Uses conversation policies to elaborate on agent-to-agent communication
• OAA Model
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MAS Architectures (3)
• General Magic’s Model – A commercial agent technology for electronic commerce– Views MAS as an electronic marketplace– The marketplace is modeled as a network of computers supporting a collection of
places that offer services to mobile agents– The mobile agents:
• can travel, meet other agents, create connections to other places• they have authority
• Zeus: a MAS development toolkit
A
B
C
D
Agent Facilitator
Abilit ies Database
Agent Name Server
Address Book
request
reply
Transport Protocol
MESSAGE
Common Message Format (Language)
Shared mesage content representation and ontology
Agent
Perform Task A
Agent
Perform Task C
Agent
Perform Task D
External program
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MAS Architectures (4)
• Geo-Agents (GIS agents) Architecture
Geo-Agents
Domain (Service) Agent
Domain (Service) AgentTask Agent
Facilitator
Administrator UI Agent
Task Agent
Other Agent Systems User
Query agent
Exchange reg-istry
Query agent
Query agent
Query agent Pass taskReply
Coordi-nate
Coordi-nate
Collabo-rate
Control/Re-ply
Task(GeoScrip
t)Reply
Collabo-rate
Data sources
Retrieve
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Coordination
• Coordination: a process to manage dependencies among activities
• Three aspects of coordination– Activity aspect
• What activity to execute?• When an activity should be executed?• Model to coordinate distributed tasks: Statecharts, Flowcharts, Process algebra, Lotos,
SDL, Estelle …– Conversation (state) aspect
• What is the structure of the conversation among the coordinating entities?• FSM, Petri-Nets, State Transition Diagrams
– Implementation aspect• How to implement distributed software systems where software components coordinate
their actions
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KQML
• Knowledge Query and Manipulation Language (KQML) is both a message format and a message-handling protocol to support run-time knowledge sharing among agents
• KQML comprise a substrate on which to develop higher-level models of inter-agent interaction such as contract nets
• KQML is a coordination mechanism from the conversation aspect
• KQML contains an extensible set of performatives, which defines the permissible speech acts agents may use
• Example performative:
Coordination
(ask-all /* message layer */ :content "price(IBM, [?price, ?time])“
/* content layer */ :receiver stock-server
/* communication layer */ :language standard_prolog :ontology NYSE-TICKS
:sender me)
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KQML: Types of Performatives
• Basic informative performatives: tell, deny, … • Database performatives: insert, delete, … • Basic responses: error, sorry, … • Basic query performatives: ask-one, ask-all, evaluate,… • Multi-response query performatives: stream-all, …• Basic effector performatives: achieve, … • Generator performatives: standby, ready, next, … • Capability-definition performatives: advertise • Notification performatives: subscribe • Networking performatives: register, forward, pipe, broadcast, … • Facilitation performatives: broker-one (all), recommend-one (all), re-
cruit-one (all)
Coordination
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Collaboration
• Collaboration refers to cooperative effort among agents to reach a single goal by exchanging knowledge built upon the underlying coor-dination mechanism
• Example mechanism: Contract Net Protocol (CNP)– Negotiation as a collaboration mechanism – Negotiation on how tasks should be shared
• A task (plan) may be decomposed in a hierarchy of subtasks (hierarchical planning)• An agent may subcontract another agent to perform a (sub)task.
Con-tract
Bid
agent agent
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Collaboration
Contractor
Potential candidate agents
Task announcement ("broadcast")
Contractor
Candidate Candidate
Bid
Bid
Phase 1: Task Announcement
- The contractor agent publicly announces a task.
- Potential candidates evaluate the task according to their won skills and availability.
Phase 2: Submission of Bids / Proposals
- Agents that satisfy the requiremenst, i.e., are able to perform the task, send their bid / proposal to the contractor.
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Collaboration
Contractor
Selected candidate
Contractor
Contracted agent
Contract
Phase 3: Selection
- The selection of the best candidate is made by the contractor based on received bids and on the CVs of the candidates.
Phase 4: Contract awarding- A contract is established between the contractor and the selected candidate.
- A privileged bilateral communication channel is established between the two agents.
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Several Issues in Designing Competitive MAS
• Distributed rationality
• Pareto optimality
• Stability
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Distributed Rationality
• Techniques to encourage/coax/force self-interested agents to play fairly in the sandbox
– Voting: Everybody’s opinion counts (but how much?)– Auctions: Everybody gets a chance to earn value (but how to do it fairly?)– Contract nets: Work goes to the highest bidder– Issues:
• Global utility• Fairness• Stability• Cheating and lying
Competitive MAS
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Pareto Optimality
• S is a Pareto-optimal solution iff– S’ ( x Ux(S’) > Ux(S) → y Uy(S’) < Uy(S))– i.e., if X is better off in S’, then some Y must be worse off
• Social welfare, or global utility, is the sum of all agents’ utility– If S maximizes social welfare, it is also Pareto-optimal (but not vice versa)
Competitive MAS
X’s utility
Y’s utility
Which solutionsare Pareto-optimal?
Which solutionsmaximize global utility(social welfare)?
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Stability
• If an agent can always maximize its utility with a particular strategy (regardless of other agents’ behavior) then that strategy is dominant
• A set of agent strategies is in Nash equilibrium if each agent’s strat-egy Si is locally optimal, given the other agents’ strategies
– No agent has an incentive to change strategies– Hence this set of strategies is locally stable
• Prisoner’s dilemma– Pareto-optimal and social welfare maximizing solution: Both agents cooperate– Dominant strategy and Nash equilibrium: Both agents defect
Competitive MAS
Cooperate Defect
Cooperate 3, 3 0, 5
Defect 5, 0 1, 1
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Development of MAS
• Define the organization of the MAS according to the problem specifi-cation (or solution structure)
• Decide the coordination mechanism• Select a MAS implementation framework, e.g., Zeus, that supports
the coordination mechanism• Implement the collaborative mechanism which support the MAS or-
ganization• Implement shared ontology • Implement each task agent (including customizing associated com-
munication module)• Customize middle agents
– Facilitators– Mediators– Brokers– Matchmakers and yellow pages– Blackboards
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Applications of MAS
• Advanced Manufacturing Management Systems– Agents as representatives of machines, users, business processes, etc.
• Intelligent Information Search on Internet– Some agents may show learning capabilities (learn the preferences of their
users, ..)
• Intelligent security enforcement on Internet– Agents are representative of sensors or IDSs
• Shopping Agents in Electronic Commerce– With search, price comparison, and bargaining capabilities
• Multi-agent auction in E-commerce• Distributed Surveillance
– For information search or to look for special events informing their users of relevant news
• Distributed Signal Processing– For problem diagnosis, situation assessment, etc. in the network
• Distributed Problem Solving– Collaborative design, scheduling, and planning
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Agent Organizations
• Multiple (human and/or artificial) agents
• Goal-directed (goals may be dynamic and/or conflicting)
• Affects and is affected by the environment
• Has knowledge, culture, memories, history, and capabilities (distinct from individual agents)
• Legal standing is distinct from single agent
• Q: How are MAS organizations different from human organizations?
MAS Research Directions
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Organizational Structures
• Exploit structure of task decomposition– Establish “channels of communication” among agents working on related subtasks
• Organizational structure:– Defines (or describes) roles, responsibilities, and preferences– Use to identify control and communication patterns:
• Who does what for whom: Where to send which task announcements/allocations• Who needs to know what: Where to send which partial or complete results
MAS Research Directions
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Communication
• Communication models– Theoretical models: Speech act theory– Practical models:
• Shared languages like KIF, KQML, DAML• Service models like DAML-S• Social convention protocols
• Communication strategies– Connectivity (network topology) strongly influences the effectiveness of an organi-
zation– Changes in connectivity over time can impact team performance:
• Move out of communication range coordination failures• Changes in network structure reduced (or increased) bandwidth, increased (or reduced)
latency
MAS Research Directions
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Learning in MAS
• Emerging field to investigate how teams of agents can learn individu-ally and as groups
• Distributed reinforcement learning– Behave as an individual, receive team feedback, and learn to individually contribute
to team performance– Iteratively allocate “credit” for group performance to individual decisions
• Genetic algorithms: Evolve a society of agents (survival of the fittest)
• Strategy learning: In market environments, learn other agents’ strate-gies
MAS Research Directions
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Adaptive Organizational Dynamics
• Potential for change:– Change parameters of organization over time– That is, change the structures, add/delete/move agents, …
• Adaptation techniques:– Genetic algorithms– Neural networks– Heuristic search / simulated annealing– Design of new processes and procedures– Adaptation of individual agents
MAS Research Directions
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Summary
• “Agent” means many different things
• Different types of “multi-agent systems”:– Cooperative vs. competitive– Heterogeneous vs. homogeneous– Micro vs. macro
• Lots of interesting/open research directions:– Effective cooperation strategies– “Fair” coordination strategies and protocols– Learning in MAS– Resource-limited MAS (communication, …)
• Next lecture– Communication & Platform
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