How can an agent learn to negotiate (1997)
| Venue: | Intelligent Agents III. Agent Theories, Architectures, and Languages, number 1193 in LNAI |
| Citations: | 21 - 1 self |
BibTeX
@INPROCEEDINGS{Zeng97howcan,
author = {Dajun Zeng and Katia Sycara},
title = {How can an agent learn to negotiate},
booktitle = {Intelligent Agents III. Agent Theories, Architectures, and Languages, number 1193 in LNAI},
year = {1997},
pages = {233--244},
publisher = {Springer Verlag}
}
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Abstract
Negotiation has been extensively discussed in game-theoretic, economic, and management science literatures for decades. Recent growing interest in autonomous interacting software agents and their potential application in areas such as electronic commerce has given increased importance to automated negotiation. Evidence both from theoretical analysis and from observations of human interactions suggests that if decision makers can somehow take into consideration what other agents are thinking and furthermore learn during their interactions how other agents behave, their payo might increase. In this paper, we propose a sequential decision making model of negotiation, called Bazaar. It provides an adaptive, multi-issue negotiation model capable of exhibiting a rich set of negotiation behaviors. Within the proposed negotiation framework, we model learning as aBayesian belief update process. In this paper, we present both theoretical analysis and initial experimental results showing that learning is bene cial in the sequential negotiation model. 1
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