Readings in agents
A dynamic mechanism for time-constrained trading
Proceedings of the fifth international conference on Autonomous agents
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms for Automated Negotiations: A FSM-Based Application Approach
DEXA '00 Proceedings of the 11th International Workshop on Database and Expert Systems Applications
On artificial agents for negotiation in electronic commerce
On artificial agents for negotiation in electronic commerce
Benefits of learning in negotiation
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Agent behaviors in virtual negotiation environments
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Automated negotiation has become increasingly important since the advent of electronic commerce. In an efficient market, goods are not necessarily traded in 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 genetic 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 based upon the stochastic approximation. We also consider two types of agents: level- 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 parties' payoff.