Social and Cognitive System for Learning Negotiation Strategies with Incomplete Information

Authors:
Amine Chohra;Arash Bahrammirzaee;Kurosh Madani
Affiliations:
Images, Signals, and Intelligent Systems Laboratory (LISSI / EA 3956), Paris-XII University, Senart Institute of Technology, Lieusaint, France 77127;Images, Signals, and Intelligent Systems Laboratory (LISSI / EA 3956), Paris-XII University, Senart Institute of Technology, Lieusaint, France 77127;Images, Signals, and Intelligent Systems Laboratory (LISSI / EA 3956), Paris-XII University, Senart Institute of Technology, Lieusaint, France 77127
Venue:
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Year:
2009

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Abstract

Finding adequate (win-win solutions for both parties) negotiation strategy with incomplete information for autonomous agents, even in one-to-one negotiation, is a complex problem. First part of this paper aims to develop negotiation strategies for autonomous agents with incomplete information, where negotiation behaviors, based on time-dependent behaviors, are suggested to be used in combination (inspired from empirical human negotiation research). Suggested combination allows agents to improve negotiation process in terms of agent utilities, round number to reach an agreement, and percentage of agreements. Second part aims to develop a social and cognitive system for learning negotiation strategies from interaction, where characters conciliatory, neutral, or aggressive, are suggested to be integrated in negotiation behaviors (inspired from research works aiming to analyze human behavior and those on social negotiation psychology). Suggested strategy displays ability to provide agents, through a basic buying strategy, with a first intelligence level in social and cognitive system to learn from interaction (human-agent or agent-agent).