Auctions for robotics panel: talking points - PowerPoint PPT Presentation

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Auctions for robotics panel: talking points

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Elicitor. Conen & Sandholm IJCAI-01 workshop on Econ. Agents, Models ... Elicitor decides what to ask next based on answers it has received so far $ 1,000 for ... – PowerPoint PPT presentation

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Title: Auctions for robotics panel: talking points


1
Auctions for robotics panel talking points
  • Robotics setting
  • Incentives usually dont matter
  • Problems are combinatorial/multi-attribute
  • gt modern work on complex auctions exchanges
    can be helpful
  • Similar to other MAS (at least from a
    coordination perspective)

2
Peer-to-peer negotiation
  • Marginal cost based contracting AAAI-93,
    ICMAS-95, PhD-96
  • Automated cost computation
  • Issues emerging from distributed implementation
  • Parellellism vs monotonicity
  • Avoiding msg saturation
  • Termination,
  • Contracting as hill-climbing AAAI SS-98
  • OCSM-contracts AAAI SS-98, ICMAS-98, AAAI-99,
    ICDCS-00
  • Leveled commitment contracts ICMAS-95, AAAI-96,
    IJCAI-99, GEB-01, AIJ-02
  • Sequences cascades ICMAS-98, J. Econ. Dynamics
    Control-01

3
Mediated markets
  • Removes negotiation process uncertainty gt better
    allocations
  • Usually faster as well
  • Package bidding ? expressive competition DCR-01,
    GEB-06, Interfaces-06, IAAI-06,
  • Rich forms of offer constructs
  • Side constraints
  • Multi-attribute functionality
  • Preference elicitation from the different parties
    (studied for CAs CEs already) EC-01, AAAI-02,
    EC-03,
  • Focuses the agents marginal cost/value
    computations

4
Deliberation control
  • Heuristically in peer-to-peer negotiation
    AAAI-93, ICMAS-95, PhD-96
  • Game-theoretically
  • Impossibility results ICMAS-96, ICEC-00,
    AAMAS-05
  • Using performance profile trees
  • in auctions TARK-01, AGENTS WS-01, AAMAS-03,
    AAMAS-04
  • in bargaining AIJ-01, AAMAS-02

5
Online problem
  • Has been studied for multi-unit
  • auctions Lavi Nisan EC-00,
  • exchanges Blum, Sandholm, Zinkevich SODA-02,
    JACM-06
  • Thank you for your attention!

6
Preference elicitationfrom multiple agents
7
Monsters
  • Local planning complexity
  • Communication complexity
  • (Loss of privacy)

8
What info is needed from an agent depends on what
others have revealed
Elicitor
Clearing algorithm
Elicitor decides what to ask next based on
answers it has received so far
Conen Sandholm IJCAI-01 workshop on Econ.
Agents, Models Mechanisms, ACMEC-01
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