multi-agent systems: overview and research directions cmsc 477/677 spring 2005 prof. marie...
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Multi-Agent Systems:Overview and Research Directions
CMSC 477/677
Spring 2005
Prof. Marie desJardins
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Outline
Agent Architectures Logical Cognitive Reactive Theories of Mind
Multi-Agent Systems Cooperative multi-agent systems Competitive multi-agent systems
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Agent Architectures
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Agent Architectures
Logical Architectures Cognitive Architectures Reactive Architectures Theories of Mind
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Logical Architectures
Formal models of reasoning and agent interaction GOLOG*: Logic programming language BDI Models: Explicitly model beliefs, desires, and intentions
of agents
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Cognitive Architectures
Computational models of human cognition ACT-R*, Soar*: Production rule architectures, very human-
inspired PRODIGY*: Planning-centric architecture, focused on
learning, less human-inspired APEX*: “Sketchy planning;” focus on human performance in
multitasking, action selection, resource limitations
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Reactive Architectures
Perceive and react (a.k.a. “Representation, schmepresentation!”)
Brooks: The original reactivist PENGI: Reactive video game player AuRA: Hybrid deliberative/reactive robot architecture
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Theories of Mind
Forays into philosophy and cognitive psychology Society of Mind (Minsky): The brain is a collection of
autonomous agents, all working in harmony Emotion: Do we need emotions to behave like humans, or
to interact with humans? Consciousness: What is it? Where does it come from? Will
our AIs ever have it?
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Multi-Agent Systems
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Multi-agent systems
Jennings et al.’s key properties: Situated Autonomous Flexible:
Responsive to dynamic environment Pro-active / goal-directed Social interactions with other agents and humans
Research questions: How do we design agents to interact effectively to solve a wide range of problems in many different environments?
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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
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Topics in multi-agent systems
Cooperative MAS: Distributed problem solving: Less autonomy Distributed planning: Models for cooperation and teamwork
Competitive or self-interested MAS: Distributed rationality: Voting, auctions Negotiation: Contract nets
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Typical (cooperative) MAS domains
Distributed sensor network establishment Distributed vehicle monitoring Distributed delivery
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Cooperative Multi-Agent Systems
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Distributed problem solving/planning
Cooperative agents, working together to solve complex problems with local information
Partial Global Planning (PGP): A planning-centric distributed architecture
SharedPlans: A formal model for joint activity Joint Intentions: Another formal model for joint activity STEAM: Distributed teamwork; influenced by joint
intentions and SharedPlans
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Distributed problem solving
Problem solving in the classical AI sense, distributed among multiple agents That is, formulating a solution/answer to some complex question Agents may be heterogeneous or homogeneous DPS implies that agents must be cooperative (or, if self-interested,
then rewarded for working together)
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Competitive Multi-Agent Systems
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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
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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)
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 strategy 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
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Prisoner’s Dilemma
Cooperate Defect
Cooperate 3, 3 0, 5
Defect 5, 0 1, 1
AB
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Prisoner’s Dilemma: Analysis
Pareto-optimal and social welfare maximizing solution: Both agents cooperate
Dominant strategy and Nash equilibrium: Both agents defect
Cooperate Defect
Cooperate 3, 3 0, 5
Defect 5, 0 1, 1
Why?
AB
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Voting
How should we rank the possible outcomes, given individual agents’ preferences (votes)?
Six desirable properties (which can’t all simultaneously be satisfied): Every combination of votes should lead to a ranking Every pair of outcomes should have a relative ranking The ranking should be asymmetric and transitive The ranking should be Pareto-optimal Irrelevant alternatives shouldn’t influence the outcome Share the wealth: No agent should always get their way
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Voting protocols
Plurality voting: the outcome with the highest number of votes wins Irrelevant alternatives can change the outcome: The Ross Perot factor
Borda voting: Agents’ rankings are used as weights, which are summed across all agents Agents can “spend” high rankings on losing choices, making their remaining
votes less influential Binary voting: Agents rank sequential pairs of choices (“elimination
voting”) Irrelevant alternatives can still change the outcome Very order-dependent
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Auctions
Many different types and protocols All of the common protocols yield Pareto-optimal outcomes But… Bidders can agree to artificially lower prices in order
to cheat the auctioneer What about when the colluders cheat each other?
(Now that’s really not playing nicely in the sandbox!)
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Contract nets
Simple form of negotiation Announce tasks, receive bids, award contracts Many variations: directed contracts, timeouts, bundling of
contracts, sharing of contracts, … There are also more sophisticated dialogue-based
negotiation models
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Conclusions and directions
“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, …)