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Multi-Agent Systems: Overview and Research Directions

This document provides an introduction to multi-agent systems, exploring different agent architectures and theories of mind. It discusses cooperative and competitive multi-agent systems, as well as various topics and domains within this field of study.

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Multi-Agent Systems: Overview and Research Directions

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  1. Multi-Agent Systems:Overview and Research Directions CMSC 477/677 Spring 2005 Prof. Marie desJardins

  2. Outline • Agent Architectures • Logical • Cognitive • Reactive • Theories of Mind • Multi-Agent Systems • Cooperative multi-agent systems • Competitive multi-agent systems

  3. Agent Architectures

  4. Agent Architectures • Logical Architectures • Cognitive Architectures • Reactive Architectures • Theories of Mind

  5. Logical Architectures Formal models of reasoning and agent interaction • GOLOG*: Logic programming language • BDI Models: Explicitly model beliefs, desires, and intentions of agents

  6. 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

  7. 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

  8. 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?

  9. Multi-Agent Systems

  10. 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?

  11. 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

  12. 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

  13. Typical (cooperative) MAS domains • Distributed sensor network establishment • Distributed vehicle monitoring • Distributed delivery

  14. Cooperative Multi-Agent Systems

  15. 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

  16. 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)

  17. Competitive Multi-Agent Systems

  18. 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

  19. 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) Which solutions are Pareto-optimal? Y’s utility Which solutions maximize global utility (social welfare)? X’s utility

  20. 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

  21. Prisoner’s Dilemma Let's play! B A

  22. Prisoner’s Dilemma: Analysis • Pareto-optimal and social welfare maximizing solution: Both agents cooperate • Dominant strategy and Nash equilibrium: Both agents defect B A • Why?

  23. 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 

  24. 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

  25. 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!)

  26. 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

  27. 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, …)

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