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Learning the Structure of Utility Graphs Used in Multi-issue Negotiation through Collaborative Filtering

Learning the Structure of Utility Graphs Used in Multi-issue Negotiation through Collaborative Filtering
Learning the Structure of Utility Graphs Used in Multi-issue Negotiation through Collaborative Filtering
Graphical utility models represent powerful formalisms for modeling complex agent decisions involving multiple issues [2]. In the context of negotiation, it has been shown [10] that using utility graphs enables reaching Pareto-efficient agreements with a limited number of negotiation steps, even for high-dimensional negotiations involving complex complementarity/ substitutability dependencies between multiple issues. This paper considerably extends the results of [10], by proposing a method for constructing the utility graphs of buyers automatically, based on previous negotiation data. Our method is based on techniques inspired from item-based collaborative filtering, used in online recommendation algorithms. Experimental results show that our approach is able to retrieve the structure of utility graphs online, with a high degree of accuracy, even for highly non-linear settings and even if a relatively small amount of data about concluded negotiations is available.
978-3-642-03337-7
192-209
Springer Berlin, Heidelberg
Robu, Valentin
36b30550-208e-48d4-8f0e-8ff6976cf566
La Poutre, J.A.
31ffa5ed-a4a3-40d7-ac47-db7a375d95ae
Robu, Valentin
36b30550-208e-48d4-8f0e-8ff6976cf566
La Poutre, J.A.
31ffa5ed-a4a3-40d7-ac47-db7a375d95ae

Robu, Valentin and La Poutre, J.A. (2009) Learning the Structure of Utility Graphs Used in Multi-issue Negotiation through Collaborative Filtering. In, Lecture Notes in Computer Science, vol. 4078. Springer Berlin, Heidelberg, pp. 192-209.

Record type: Book Section

Abstract

Graphical utility models represent powerful formalisms for modeling complex agent decisions involving multiple issues [2]. In the context of negotiation, it has been shown [10] that using utility graphs enables reaching Pareto-efficient agreements with a limited number of negotiation steps, even for high-dimensional negotiations involving complex complementarity/ substitutability dependencies between multiple issues. This paper considerably extends the results of [10], by proposing a method for constructing the utility graphs of buyers automatically, based on previous negotiation data. Our method is based on techniques inspired from item-based collaborative filtering, used in online recommendation algorithms. Experimental results show that our approach is able to retrieve the structure of utility graphs online, with a high degree of accuracy, even for highly non-linear settings and even if a relatively small amount of data about concluded negotiations is available.

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Published date: 14 July 2009
Additional Information: Chapter: Multi
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 268206
URI: http://eprints.soton.ac.uk/id/eprint/268206
ISBN: 978-3-642-03337-7
PURE UUID: 97aa259f-6de9-4846-8455-0aa1e3668504

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Date deposited: 13 Nov 2009 14:01
Last modified: 14 Mar 2024 09:05

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Contributors

Author: Valentin Robu
Author: J.A. La Poutre

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