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 ebook constitutes the completely refereed post-proceedings of the fifth overseas 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 awarded have been rigorously chosen from 22 submissions in the course of rounds of reviewing and revision. The papers are equipped in topical sections on automatic negotiation, structures and mechanism layout, and multi-agent markets.

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Extra resources for Agent-Mediated Electronic Commerce V. Designing Mechanisms and Systems: AAMAS 2003 Workshop, AMEC 2003, Melbourne, Australia, July 15, 2003, Revised Selected Papers

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For example, Acquiring Tradeoff Preferences for Automated Negotiations: A Case Study 39 consider the case of an international student who just arrives in the UK for the first time and who has to rent some accommodation. Since he is totally new to the country, he cannot articulate precisely the details and type of accommodation he wants, but he can express his preferences as some constraints. For example, the accommodation must be placed less than 15 minutes walk from the university,1 the accommodation rental period should be less than one year, and the rental rate should be about £250.

This is the topic of the following sections. , ρ(R1f ), ρ(R2f ) and ρ(R3f )). Each is dealt with in turn. 1 Acquiring Preference Constraints According to standard business negotiation theory [32, 39], the preferences of human negotiators should be extensively explored before the actual negotiation begins. One common technique for doing this is to play simulated negotiation games in which various negotiation scenarios are enacted against various human negotiation partners. Such role playing aims to explore the negotiation space as fully as possible and to tease out the negotiator’s real preferences and tradeoffs.

Step 3. The user is then shown a visual representation of the relative priorities of the different constraints (see Fig. 6). If the user is happy with this outcome the acquisition phase is complete. If the user feels the outcome does not reflect their true priorities then they can modify their priority allocation until they are satisfied. 5 Related Work The background of our work is knowledge acquisition and preference elicitation. There are many methods for acquiring knowledge from humans for knowledgebased system in general (see [17] for an overview).

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