Abstract
Finding the adequate (win-win solutions for both parties) negotiation strategy with incomplete information for autonomous agents, even in one-toone negotiation, is a complex problem. Elsewhere, negotiation behaviors, in which the characters such as conciliatory, neutral, or aggressive define a ‘psychological’ aspect of the negotiator personality, play an important role. More, learning in negotiation is fundamental for understanding human behaviors as well as for developing new solution concepts of teaching methodologies of negotiation strategies (skills). First part of this Chapter 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). The suggested combination of behaviors allows agents to improve the negotiation process in terms of agent utilities, round number to reach an agreement, and percentage of agreements. Second part of this Chapter aims to develop a SOcial and COgnitive SYStem (SOCOSYS) for learning negotiation strategies from interaction (human-agent or agent-agent), where the 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). The suggested strategy displays the ability to provide agents, through a basic buying strategy, with a first intelligence level, with SOCOSYS to learn from interaction (human-agent or agent-agent).
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References
Rosenschein, J., Zlotkin, G.: Rules of Encounter. MIT Press, Cambridge (1994)
Wooldridge, M.: An Introduction to MultiAgent Systems. John Wiley & Sons, England (2002)
Jennings, N.R., Faratin, P., Lomuscio, A.R., Parsons, S., Sierra, C., Wooldridge, M.: Automated negotiation: prospects, methods, and challenges. Int. J. of Group Decision and Negotiation 10(2), 199–215 (2001)
Gerding, E.H., van Bragt, D., Poutré, J.L.: Scientific Approaches and Techniques for Negotiation: A Game Theoretic and Artificial Intelligence Perspective. CWI, Technical Report, SEN-R0005 (2000)
Li, C., Giampapa, J., Sycara, K.: Bilateral negotiation decisions with Uncertain Dynamic outside options. IEEE Trans. on Systems, Man, and Cybernetics, Part C: Special Issue on Game-Theoretic Analysis and Stochastic Simulation of Negotiation Agents 36(1), 1–13 (2006)
Lomuscio, A.R., Wooldridge, M., Jennings, N.R.: A classification scheme for negotiation in electronic commerce. Int. J. of Group Decision and Negotiation 12(1), 31–56 (2003)
Faratin, P., Sierra, C., Jennings, N.R.: Negotiation decision functions for autonomous agents. International Journal of Robotics and Autonomous Systems 24(3-4), 159–182 (1998)
Wang, K.J., Chou, C.H.: Evaluating NDF-based negotiation mechanism within an agent-based environment. In: Robotics and Autonomous Systems, vol. 43, pp. 1–27. Elsevier, Amsterdam (2003)
Ros, R., Sierra, C.: A negotiation meta strategy combining trade-off and concession moves. In: Auton. Agent Multi-Agent Sys., vol. 12, pp. 163–181. Springer, Heidelberg (2006)
Pruitt, D.: Negotiation Behavior. Academic Press, London (1981)
Hamner, W.: Effects of bargaining strategy and pressure to reach agreement in a stalemated negotiation. Journal of Personality and Social Psychology 30(4), 458–467 (1974)
Carnevale, P., Pruitt, D.: Negotiation and Mediation. In: Rosenzweig, M., Porter, L. (eds.) Annual Review of Psychology, vol. 43, pp. 531–581. Annual Reviews Inc (1992)
Lopes, F., Mamede, N., Novais, A.Q., Coelho, H.: A negotiation model for autonomous computational agents: Formal description and empirical evaluation. Journal of Intelligent and Fuzzy Systems 12, 195–212 (2002)
Bales, R.F.: Interaction Process Analysis: A Method for the Study of Small Groups. Addisson-Wesley, Cambridge (1950)
Rubin, J.Z., Brown, B.R.: The Social Psychology of Bargaining and Negotiation. Academic Press, New York (1975)
Silver, D., Sutton, R., Müller, M.: Reinforcement Learning of Local Shape in the Game of Go. In: Int. Joint Conference on Artificial Intelligence, pp. 1053–1058 (2007)
Miglino, O., Di Ferdinando, A., Rega, A., Benincasa, B.: SISINE: Teaching negotiation through a multiplayer online role playing game. In: The 6th European Conference on E-Learning, Copenhague, Danemark, October 04-05 (2007)
Pfeifer, R., Scheier, C.: Understanding Intelligence. MIT Press, Cambridge (1999)
Chohra, A.: Embodied cognitive science, intelligent behavior control, machine learning, soft computing, and FPGA integration: towards fast, cooperative and adversarial robot team (RoboCup). Technical GMD Report, No. 136, ISSN 1435-2702, Germany (June 2001)
Zeng, D., Sycara, K.: Benefits of learning in negotiation. In: Proc. of the 14th National Conference on Artificial Intelligence (AAAI 1997), Providence, RI, July 1997, pp. 610–618 (1997)
Watkins, C.J.C.H.: Learning from Delayed Rewards. PhD Thesis, King’s College (1989)
Whitehead, S.D.: Reinforcement Learning for the Adaptive Control of Perception and Action. Technical Report 406, University of Rochester (February 1992)
Sutton, R.S., Barto, A.G.: Reinforcement Learning. MIT Press, Cambridge (1998)
Dietterich, T.G.: Hierarchical reinforcement learning with the MAXQ value function decomposition. Journal of Artificial Intelligence Research 13, 227–303 (2000)
Anderson, J.A.: An Introduction to Neural Networks. The MIT Press, England (1995)
Patterson, D.W.: Artificial Neural Networks: Theory and Applications. Prentice-Hall, Simon & Schuster (Asia) Pte Ltd, Singapore (1996)
Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)
Lee, C.-F., Chang, P.-L.: Evaluations of tactics for automated negotiations. In: Group Decision and Negotiation. Springer, Heidelberg (2008)
Sandholm, T.W.: Distributed Rational Decision Making. In: Multiagent Systems: A Modern Introduction to Distributed Artificial Intelligence, pp. 201–258. MIT Press, Cambridge (1999)
Coggan, M.: Exploration and Exploitation in Reinforcement Learning., Research supervised by Prof. Doina Precup, CRA-W DMP Project at McGill University (2004)
Takadama, K., Fujita, H.: Lessons learned from comparison between Q-learning and Sarsa agents in bargaining game. In: North American Asso. for Computational Social and Organizational Science Conference (2004)
Lin, L.-J.: Self-improving reactive agents based on reinforcement learning, planning and teaching. In: Machine Learning, vol. 8, pp. 293–321. Kluwer Academic Publishers, Dordrecht (1992)
Touzet, C.F.: Neural reinforcement learning for behaviour synthesis. Robotics and Autonomous Systems 22, 251–281 (1997)
Carpenter, G.A., Grossberg, S., Rosen, D.B.: Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks 4, 759–771 (1991)
Azouaoui, O., Chohra, A.: Soft computing based pattern classifiers for the obstacle avoidance behavior of Intelligent Autonomous Vehicles (IAV). Int. Journal of Applied Intelligence 16(3), 249–271 (2002)
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Chohra, A., Bahrammirzaee, A., Madani, K. (2010). Negotiation Strategies with Incomplete Information and Social and Cognitive System for Intelligent Human-Agent Interaction. In: Szczerbicki, E., Nguyen, N.T. (eds) Smart Information and Knowledge Management. Studies in Computational Intelligence, vol 260. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04584-4_7
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DOI: https://doi.org/10.1007/978-3-642-04584-4_7
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