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Learning an opponent's preferences to make effective multi-issue negotiation tradeoffs

Learning an opponent's preferences to make effective multi-issue negotiation tradeoffs
Learning an opponent's preferences to make effective multi-issue negotiation tradeoffs
Software agents that autonomously act and interact to achieve their design objectives are increasingly being developed for a range of e-commerce applications. In this context, automated negotiation is a central concern since it is the de facto means of establishing contracts for goods or services between the agents. Now, in many 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 multi-issue 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,we explore and evaluate the use of kernel density estimation for this purpose. Specifically, we couch our work in the context of making negotiation trade-offs and show how our approach can make the negotiation outcome more efficient for both participants.
59-68
Coehoorn, R. M.
056f466a-dc6a-4be0-87b8-4c949db27917
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Coehoorn, R. M.
056f466a-dc6a-4be0-87b8-4c949db27917
Jennings, N. R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Coehoorn, R. M. and Jennings, N. R. (2004) Learning an opponent's preferences to make effective multi-issue negotiation tradeoffs. 6th International Conference on E-Commerce, Delft, The, Netherlands. pp. 59-68 .

Record type: Conference or Workshop Item (Paper)

Abstract

Software agents that autonomously act and interact to achieve their design objectives are increasingly being developed for a range of e-commerce applications. In this context, automated negotiation is a central concern since it is the de facto means of establishing contracts for goods or services between the agents. Now, in many 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 multi-issue 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,we explore and evaluate the use of kernel density estimation for this purpose. Specifically, we couch our work in the context of making negotiation trade-offs and show how our approach can make the negotiation outcome more efficient for both participants.

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More information

Published date: 2004
Additional Information: Event Dates: 2004
Venue - Dates: 6th International Conference on E-Commerce, Delft, The, Netherlands, 2004-01-01
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 259549
URI: http://eprints.soton.ac.uk/id/eprint/259549
PURE UUID: af9faac3-daad-4fe6-a375-9aa1d4ce0e52

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Date deposited: 06 Sep 2004
Last modified: 23 Sep 2020 16:33

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