Reward and Penalty Functions in Automated Negotiation
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AbstractAutomated negotiation is very important for organizing decentralized systems such as e-business, p2p systems, cloud computing, and so on. During the course of a negotiation, reward and penalty can be used to increase the chance of reaching agreements between negotiating agents, but have not been applied into automated negotiation systems well, especially integrating both in a single negotiation system. Thus, in this work we make an effort to reveal how the reward increases the acceptability of an offer and how the penalty decreases the deniability of an offer. More specifically, our study shows that the degree, to which a reward and a penalty influence the outcome, depends on the greedy degree for the reward and the creditable degree on the penalty. Therefore, if we know an offeree's utilities of accepting and denying an offer, the greedy degree for reward and the creditable degree on penalty, we can calculate how much reward and penalty the offerer agent needs to change the offeree's mind (i.e., from denying to accepting). (c) 2015 Wiley Periodicals, Inc.
All Author(s) ListLuo XD, Yang YF, Leung HF
Journal nameInternational Journal of Intelligent Systems
Volume Number31
Issue Number7
Pages637 - 672
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence

Last updated on 2020-05-08 at 04:51