Understanding intelligence
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Scientific approaches and techniques for negotiation. A game theoretic and artificial intelligence perspective
Multistage Fuzzy Decision Making in Bilateral Negotiation with Finite Termination Times
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Benefits of learning in negotiation
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Bilateral negotiation decisions with uncertain dynamic outside options
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Finding adequate negotiation strategy with incomplete information, even in one to one negotiation, is a complex problem. Inspired from research works aiming to analyze human behavior and those on social negotiation psychology, integration of psychological aspects, with essential time parameter, is becoming necessary. For this purpose, first, one to one bargaining process, in which a buyer agent and a seller agent negotiate over single issue (price), is developed, where social and cognitive behaviors based on time (Faratin et al. 1998) and personality aspects are suggested. Second, experimental environments and measures, allowing a set of experiments, carried out for different negotiation deadlines, are detailed. Third, experimental results are analyzed with regard to time dependent behaviors. Results demonstrate that more increasing conciliatory aspects lead to increased agreement point (price) and decreased agreement time, and more increasing aggressive aspects lead to decreased agreement point and increased agreement time.