A genetic agent-based negotiation system

Automated negotiation has become increasingly important since the advent of electronic commerce. Nowadays, goods are no longer necessarily traded at a fixed price, and instead buyers and sellers negotiate among themselves to reach a deal that maximizes the payoffs of both parties. In this paper, a g enetic agent-based model for bilateral, multi-issue negotiation is studied. The negotiation agent employs genetic algorithms and attempts to learn its opponent's preferences according to the history of the counter-offers base d upon stochastic approximation. We also consider two types of agents: leve l-0 agents are only concerned with their own interest while level-1 agents consider also their opponents' utility. Our goal is to develop an automated negotiator that guides the negotiation process so as to maximize both part ies' payoff. (C) 2001 Elsevier Science B.V. All rights reserved.