Publication - Evolving the Best-response Strategy to Decide When to Make a Proposal

Authors: An, Bo; Sim, Kwang Mong; Lesser, Victor
Title: Evolving the Best-response Strategy to Decide When to Make a Proposal
Abstract: This paper designed and developed negotiation agents with the distinguishing features of 1) conducting continuous time negotiation rather than discrete time negotiation, 2) learning the response times of trading parties using Bayesian learning, and 3) deciding when to make a proposal using a multi-objective genetic algorithm (MOGA) to evolve their best-response proposing time strategies for different negotiation environments and constraints. Results from a series of experiments suggest that 1) learning trading parties‘ response times helps agents achieve more favorable trading results, and 2) on average, when compared with SSAs (Static Strategy Agents), BRSAs (Best-Response proposing time Strategy Agents) achieved higher average utilities, higher success rates in reaching deals, and smaller average negotiation time.
Keywords: Distributed AI, Learning, Multi-Agent Systems, Negotiation
Publication: 2007 IEEE Congress on Evolutionary Computation, pp. 1035 - 1042
Date: September 2007
Sources: PDF: /Documents/ban_IEEE-CEC-07.pdf
Reference: An, Bo; Sim, Kwang Mong; Lesser, Victor. Evolving the Best-response Strategy to Decide When to Make a Proposal. 2007 IEEE Congress on Evolutionary Computation, pp. 1035-1042. September 2007.
bibtex: 
@inproceedings{An-437,
  author    = "Bo An and Kwang Mong Sim and Victor Lesser",
  title     = "{Evolving the Best-response Strategy to Decide When
               to Make a Proposal}",
  booktitle = "2007 IEEE Congress on Evolutionary Computation",
  pages     = "1035-1042",
  month     = "September",
  year      = "2007",
  url       = "http://mas.cs.umass.edu/paper/437",
}