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Complex Automated Negotiations have been widely studied and are becoming an important, emerging area in the field of Autonomous Agents and Multi-Agent Systems. In general, automated negotiations can be complex, since there are a lot of factors that characterize such negotiations. These factors include the number of issues, dependency between issues, representation of utility, negotiation protocol, negotiation form (bilateral or multi-party), time constraints, etc. Software agents can support automation or simulation of such complex negotiations on the behalf of their owners, and can provide them with adequate bargaining strategies. In many multi-issue bargaining settings, negotiation becomes more than a zero-sum game, so bargaining agents have an incentive to cooperate in order to achieve efficient win-win agreements. Also, in a complex negotiation, there could be multiple issues that are interdependent. Thus, agent's utility will become more complex than simple utility functions. Further, negotiation forms and protocols could be different between bilateral situations and multi-party situations. To realize such a complex automated negotiation, we have to incorporate advanced Artificial Intelligence technologies includes search, CSP, graphical utility models, Bays nets, auctions, utility graphs, predicting and learning methods. Applications could include e-commerce tools, decision-making support tools, negotiation support tools, collaboration tools, etc. In this book, we solicit papers on all aspects of such complex automated negotiations in the field of Autonomous Agents and Multi-Agent Systems. In addition, this book includes papers on the ANAC 2010 (Automated Negotiating Agents Competition), in which automated agents who have different negotiation strategies and implemented by different developers are automatically negotiate in the several negotiation domains. ANAC is one of real testbeds in which strategies for automated negotiating agents are evaluated in atournament style.
Table des matières
Simulation of Sequential Auction Markets Using Priced Options to Reduce Bidder Exposure.- Desire-Based Negotiation in Electronic Marketplaces.- The Influence of Culture on ABMP Negotiation Parameters.- Supporting the Design of General Automated Negotiators.- Common Testbed Generating Tool based on XML for Multiple Interdependent Issues Negotiation Problems.- Simulation of Sequential Auction Markets Using Priced Options to Reduce Bidder Exposure.- Constraint and Bid Quality Factor for Bidding and Deal Identification in Complex Automated Negotiations.- Acting While Negotiating in the Convoy Formation Problem.- Multi-player Multi-issue Negotiation with Complete Information.- Automated Negotiation through a Cooperative-Competitive Model.- An Approximation method for Power Indices for Voting Games.
Résumé
Complex Automated Negotiations have been widely studied and are becoming an important, emerging area in the field of Autonomous Agents and Multi-Agent Systems. In general, automated negotiations can be complex, since there are a lot of factors that characterize such negotiations. These factors include the number of issues, dependency between issues, representation of utility, negotiation protocol, negotiation form (bilateral or multi-party), time constraints, etc. Software agents can support automation or simulation of such complex negotiations on the behalf of their owners, and can provide them with adequate bargaining strategies. In many multi-issue bargaining settings, negotiation becomes more than a zero-sum game, so bargaining agents have an incentive to cooperate in order to achieve efficient win-win agreements. Also, in a complex negotiation, there could be multiple issues that are interdependent. Thus, agent's utility will become more complex than simple utility functions. Further, negotiation forms and protocols could be different between bilateral situations and multi-party situations. To realize such a complex automated negotiation, we have to incorporate advanced Artificial Intelligence technologies includes search, CSP, graphical utility models, Bays nets, auctions, utility graphs, predicting and learning methods. Applications could include e-commerce tools, decision-making support tools, negotiation support tools, collaboration tools, etc.
In this book, we solicit papers on all aspects of such complex automated negotiations in the field of Autonomous Agents and Multi-Agent Systems. In addition, this book includes papers on the ANAC 2010 (Automated Negotiating Agents Competition), in which automated agents who have different negotiation strategies and implemented by different developers are automatically negotiate in the several negotiation domains. ANAC is one of real testbeds in which strategies for automated negotiating agents are evaluated in atournament style.
