Abstract

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.

Keywords: Tradeoff strategy and preference; Knowledge acquisition; Preference acquisition; Automated negotiation; Software agents

Article Outline

1. Introduction

2. Representing user tradeoff strategies and preferences

3. The default-then-adjust acquisition method

4. Prototype for an accommodation renting scenario

4.1. Choosing which attributes to tradeoff

4.2. Shaping the tradeoff strategy

4.3. Shaping the tradeoff preference

4.4. General remarks

5. Empirical evaluation

5.1. Subjective opinions

5.2. Accuracy versus efficiency

6. Related work

7. Conclusions and future work

Acknowledgements

References