Coehoorn, R.M. (2004) Learning an Opponent's Preferences in order to make effective Multi-issue Negotiation Trade-offs. Master's Thesis / Essay, Artificial Intelligence.
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Abstract
Software agents that autonomously act and interact to achieve their design objectives are increasingly being developed for a range of electronic commerce applications (i.e. commercial activity conducted via electronic media). Also. within an agent-oriented view of computation, it is readily apparent that most. problems require or involve multiple agents which have to interact. In this context, automated negotiation is a central concern since it is the de facto means of establishing contracts for goods or services between agents. In manv of these cases, these contracts consist of multiple issues (e.g. price. time of delivery, quantity, quality) which makes the negotiation more complex than when dealing with just price. In particular, effective and efficient multiissue negotiation requires an agent to have some indication of its opponent's preferences over these issues. However, in competitive domains, such as e-commerce. an agent will not reveal this information and so the best that can be achieved is to learn some approximation of it. through the negotiation exchanges. To this end. the use of a statistical method, kernel density estimation, was explored and evaluated. Specifically, the work is couched in the context of making negotiation tradeoffs: Giving in on one issue, while simultaneously demanding more on another. This approach proved to make the negotiation outcome more efficient for both participants.
| Item Type: | Thesis (Master's Thesis / Essay) |
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| Degree programme: | Artificial Intelligence |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 15 Feb 2018 07:30 |
| Last Modified: | 15 Feb 2018 07:30 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/8996 |
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