Reaching Agreements-Multiagent Systems-Lecture Slides, Slides of Multiagent Systems

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LECTURE 7:

Reaching

Agreements

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Reaching Agreements

• How do agents reaching agreements when they are self interested?
• In an extreme case (zero sum encounter) no agreement is possible — but in most scenarios, there is potential for mutually beneficial agreement on matters of common interest
• The capabilities of negotiation and argumentation are central to the ability of an agent to reach such agreements

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Mechanism Design

• Desirable properties of mechanisms:
  • Convergence/guaranteed success
  • Maximizing social welfare
  • Pareto efficiency
  • Individual rationality
  • Stability
  • Simplicity
  • Distribution

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Auctions

• An auction takes place between an agent known as the auctioneer and a collection of agents known as the bidders
• The goal of the auction is for the auctioneer to allocate the good to one of the bidders
• In most settings the auctioneer desires to maximize the price; bidders desire to minimize price

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English Auctions

• Most commonly known type of auction:
  • first price
  • open cry
  • ascending
• Dominant strategy is for agent to successively bid a small amount more than the current highest bid until it reaches their valuation, then withdraw
• Susceptible to:
  • winner’s curse
  • shills

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Dutch Auctions

• Dutch auctions are examples of open-cry descending auctions:
  • auctioneer starts by offering good at artificially high value
  • auctioneer lowers offer price until some agent makes a bid equal to the current offer price
  • the good is then allocated to the agent that made the offer

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Vickrey Auctions

• Vickrey auctions are:
  • second-price
  • sealed-bid
• Good is awarded to the agent that made the highest bid; at the price of the second highest bid
• Bidding to your true valuation is dominant strategy in Vickrey auctions
• Vickrey auctions susceptible to antisocial behavior

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Lies and Collusion

• The various auction protocols are susceptible to lying on the part of the auctioneer, and collusion among bidders, to varying degrees
• All four auctions (English, Dutch, First-Price Sealed Bid, Vickrey) can be manipulated by bidder collusion
• A dishonest auctioneer can exploit the Vickrey auction by lying about the 2nd-highest bid
• Shills can be introduced to inflate bidding prices in English auctions

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Negotiation in Task-Oriented DomainsImagine that you have three children, each of whom needs to be

delivered to a different school each morning. Your neighbor has four children, and also needs to take them to school. Delivery of each child can be modeled as an indivisible task. You and your neighbor can discuss the situation, and come to an agreement that it is better for both of you (for example, by carrying the other’s child to a shared destination, saving him the trip). There is no concern about being able to achieve your task by yourself. The worst that can happen is that you and your neighbor won’t come to an agreement about setting up a car pool, in which case you are no worse off than if you were alone. You can only benefit (or do no worse) from your neighbor’s tasks. Assume, though, that one of my children and one of my neighbors’ children both go to the same school (that is, the cost of carrying out these two deliveries, or two tasks, is the same as the cost of carrying out one of them). It obviously makes sense for both children to be taken together, and only my neighbor or I will need to make the trip to carry out both tasks. (^) 7-

--- Rules of Encounter , Rosenschein and Zlotkin, 1994

Machines Controlling and

Sharing Resources

• Electrical grids (load balancing)
• Telecommunications networks (routing)
• PDA’s (schedulers)
• Shared databases (intelligent access)
• Traffic control (coordination)

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The Aim of the Research

• Social engineering for communities of machines - The creation of interaction environments that foster certain kinds of social behavior

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The exploitation of game theory tools for high-level protocol design

Broad Working Assumption

• Designers (from different companies, countries, etc.) come together to agree on standards for how their automated agents will interact (in a given domain)
• Discuss various possibilities and their tradeoffs, and agree on protocols, strategies, and social laws to be implemented in their machines

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Distributed Artificial Intelligence

(DAI)

• Distributed Problem Solving (DPS)
  • Centrally designed systems, built-in cooperation, have global problem to solve

 Multi-Agent Systems (MAS) Group of utility-maximizing heterogeneous agents co-existing in same environment, possibly competitive

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Phone Call Competition

Example

• Customer wishes to place long-distance call
• Carriers simultaneously bid, sending proposed prices
• Phone automatically chooses the carrier (dynamically)

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MCI AT&T Sprint

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