Abstract
In bilateral multi-issue negotiations involving two-sided information uncertainty, selfish agents participating in a distributed search of the solution space need to learn the opponent’s preferences from the on-going negotiation interactions and utilize such knowledge to construct future proposals in order to hope to arrive at efficient outcomes. Besides, negotiation support systems that inhibit strategic misrepresentation of information need to be in place in order to assist the protagonists to obtain truly efficient solutions. To this end, this work suggests an automated negotiation procedure that while protecting the information privacy of the participating agents encourages truthful revelation of information through successive proposals. Further we present an algorithm for proposal construction in the case of two continuous issues. When both the negotiating agents implement the algorithm the negotiation trace shall be confined to the Pareto frontier. The Pareto-optimal deal close to the Nash solution shall be located whenever such a deal exists.
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Chandrashekhar, H., Bhasker, B. (2009). Learning Agents in Automated Negotiations. In: Prasad, S.K., Routray, S., Khurana, R., Sahni, S. (eds) Information Systems, Technology and Management. ICISTM 2009. Communications in Computer and Information Science, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00405-6_31
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DOI: https://doi.org/10.1007/978-3-642-00405-6_31
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