Abstract:
This paper designed and developed negotiation agents with the distinguishing features of 1) conducting continuous time negotiation rather than discrete time negotiation, ...Show MoreMetadata
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.
Published in: 2007 IEEE Congress on Evolutionary Computation
Date of Conference: 25-28 September 2007
Date Added to IEEE Xplore: 07 January 2008
ISBN Information: