By D. J. A. Somefun, E. H. Gerding, S. Bohte, J. A. La Poutré (auth.), Peyman Faratin, David C. Parkes, Juan A. Rodríguez-Aguilar, William E. Walsh (eds.)
This e-book constitutes the completely refereed post-proceedings of the fifth foreign Workshop on Agent-Mediated digital trade, AMEC 2003, held in Melbourne, Australia in July 2003 as a part of AAMAS 2003.
The nine revised complete papers offered have been conscientiously chosen from 22 submissions in the course of rounds of reviewing and revision. The papers are geared up in topical sections on automatic negotiation, structures and mechanism layout, and multi-agent markets.
Read Online or Download Agent-Mediated Electronic Commerce V. Designing Mechanisms and Systems: AAMAS 2003 Workshop, AMEC 2003, Melbourne, Australia, July 15, 2003, Revised Selected Papers PDF
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Extra info for Agent-Mediated Electronic Commerce V. Designing Mechanisms and Systems: AAMAS 2003 Workshop, AMEC 2003, Melbourne, Australia, July 15, 2003, Revised Selected Papers
Such orderings can be captured by assigning priorities to the various fuzzy constraints. In addition, according to human negotiation theory [6, 9], performing negotiation on single point solutions (those are what the negotiating agent wants) can be viewed as positional bargaining, while revealing the constraints (that need to be met) to the negotiation partner can be viewed as interest-based negotiation. In human negotiations, usually interest-based negotiations are better than positional bargaining (the detailed discussion about the reasons can be found in [6, 9]).
This results in the following value being inserted into his preference model: ((rate, period), satisf action-degree) = (((250,260],8), very-satisf actory). Fig. 3. Screen shot for the tradeoﬀ acquisition. 2. The system changes rental rate from (250, 260] to (260, 270], then the buyer answers that in this case he only wants to rent for 6 months and that this is highly satisfactory. This results in the following value being inserted in the user’s preference model: ((rate, period),satisf action-degree) = (((260,270],6),highly-satisf actory).
Such role playing aims to explore the negotiation space as fully as possible and to tease out the negotiator’s real preferences and tradeoﬀs. Building upon these intuitions, we view the process of acquiring constraints as a negotiation game in which the knowledge acquisition system plays the role of the negotiation partner. Speciﬁcally, the system randomly generates an assignment value for each negotiation attribute (from the domain of the attribute). The system then asks the user whether it is acceptable or not, and further adjusts the assignment until it becomes unacceptable/acceptable.