Abstract
A bilateral negotiation may be seen as an interaction between two agents with the goal of reaching an agreement over a given range of issues which usually involves solving a conflict of interests between the agents. Usually, the agents taking part in the negotiation will consider different issues to be the most important ones for satisfying their goals, which allows to make issue trade-offs to search for joint gains. In particular, similarity criteria have been used to perform trade-offs in bilateral negotiations. This approach behaves differently depending on the knowledge each agent has about its counterpart, and depending on the order in which the different issues are considered. In this paper we propose two new approaches to improve the search for win-win solutions, one for complete information settings and the other for incomplete information settings. The experimental evaluation shows how our proposals improve the efficiency and optimality of the negotiation process over previous approaches.KeywordsNegotiation ProcessRandom PermutationUtility GainChild GenerationBilateral NegotiationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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