From the content: The Effect of Preference Representation on Learning Preferences in Negotiation -- Bilateral Single-Issue Negotiation Model Considering Nonlinear Utility and Time Constraint -- The Effect of Grouping Issues in Multiple Interdependent Issues Negotiation based on Cone-Constraints -- Automated Agents that Proficiently Negotiate with People: Can We Keep People out of the Evaluation Loop -- Matchmaking in Multi-attribute Auctions using a Genetic Algorithm and a Particle Swarm Approach.

Complex Automated Negotiations represent an important, emerging area in the field of Autonomous Agents and Multi-Agent Systems. Automated negotiations can be complex, since there are a lot of factors that characterize such negotiations. These factors include the number of issues, dependencies between these issues,  representation of utilities, the negotiation protocol, the number of parties in the negotiation (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 efficient bargaining strategies. To realize such a complex automated negotiation, we have to incorporate advanced Artificial Intelligence technologies includes search, CSP, graphical utility models, Bayes nets, auctions, utility graphs, predicting and learning methods. Applications could include e-commerce tools, decision-making support tools, negotiation support tools, collaboration tools, etc. This book aims to provide a description of the new trends in Agent-based, Complex Automated Negotiation, based on the papers from leading researchers. Moreover, it gives an overview of the latest scientific efforts in this field, such as the platform and strategies of automated negotiating techniques.