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Acquiring User Strategies and Preferences for Negotiating Agents: A Default Then Adjust Method

Luo, X, Jennings, N. R. and Shadbolt, N. (2006) Acquiring User Strategies and Preferences for Negotiating Agents: A Default Then Adjust Method International Journal of Human-Computer Studies, 64, (4), pp. 304-321.

Record type: Article


A wide range of algorithms have been developed for various types of negotiating agents. In developing such algorithms the main focus has been on their efficiency and their effectiveness. However, this is only a part of the picture. Typically, agents negotiate on behalf of their owners and for this to be effective the agents must be able to adequately represent their owners’ strategies and preferences for negotiation. However, the process by which such knowledge is acquired is typically left unspecified. To address this problem, we undertook a study of how user information about negotiation tradeoff strategies and preferences can be captured. Specifically, we devised a novel default-then-adjust acquisition technique. In this, the system firstly does a structured interview with the user to suggest the attributes that the tradeoff could be made between, then it asks the user to adjust the suggested default tradeoff strategy by improving some attribute to see how much worse the attribute being traded off can be made while still being acceptable, and, finally, it asks the user to adjust the default preference on the tradeoff alternatives. This method is consistent with the principles of standard negotiation theory and to demonstrate its effectiveness we implemented a prototype system and performed an empirical evaluation in an accommodation renting scenario. The result of this evaluation indicates the proposed technique is helpful and efficient in accurately acquiring the users’ tradeoff strategies and preferences.

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Published date: 2006
Keywords: Tradeoff Strategy and Preference, Knowledge Aquisition, Preference Aquisition, Automated Negotiaition, Software Agents
Organisations: Web & Internet Science, Agents, Interactions & Complexity


Local EPrints ID: 264474
PURE UUID: bc804e78-9ff1-4a1a-b2ab-385368287536

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Date deposited: 06 Sep 2007
Last modified: 24 Jul 2017 16:41

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