A shopping negotiation agent that adapts to user preferences

Runhe Huang; Jianhua Ma; Qun Jin

doi:10.1007/3-540-45336-9_9

A shopping negotiation agent that adapts to user preferences

Runhe Huang, Jianhua Ma, Qun Jin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

This paper describes a shopping negotiation agent that can adapt user preferences and automatically negotiate with its counter party on behalf of a user it represents. The agent is built on a basis of the proposed negotiation model, the enhanced extended Bazaar model, which is a sequence of decision making model of negotiation with exploiting common knowledge, public information, and game theory. Since different users can have different preferences, it is important for the agent to have adaptation to different user preferences. This can be achieved by acquiring user preferences, tracing user’s behavior on Web and mapping the behavior to a set of the preference parameters, creating the negotiation model class, and generating an instance negotiation model object with new/updated preference parameters.

Original language	English
Title of host publication	Active Media Technology - 6th International Computer Science Conference, AMT 2001, Proceedings
Editors	Jiming Liu, Pong C. Yuen, Chun-hung Li, Joseph Ng, Toru Ishida
Publisher	Springer Verlag
Pages	45-56
Number of pages	12
ISBN (Electronic)	9783540430353
DOIs	https://doi.org/10.1007/3-540-45336-9_9
Publication status	Published - 2001
Externally published	Yes
Event	6th International Computer Science Conference on Active Media Technology, AMT 2001 - Hong Kong, China Duration: 2001 Dec 18 → 2001 Dec 20

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2252
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	6th International Computer Science Conference on Active Media Technology, AMT 2001
Country/Territory	China
City	Hong Kong
Period	01/12/18 → 01/12/20

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/3-540-45336-9_9

Cite this

Huang, R., Ma, J., & Jin, Q. (2001). A shopping negotiation agent that adapts to user preferences. In J. Liu, P. C. Yuen, C. Li, J. Ng, & T. Ishida (Eds.), Active Media Technology - 6th International Computer Science Conference, AMT 2001, Proceedings (pp. 45-56). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2252). Springer Verlag. https://doi.org/10.1007/3-540-45336-9_9

A shopping negotiation agent that adapts to user preferences. / Huang, Runhe; Ma, Jianhua; Jin, Qun.
Active Media Technology - 6th International Computer Science Conference, AMT 2001, Proceedings. ed. / Jiming Liu; Pong C. Yuen; Chun-hung Li; Joseph Ng; Toru Ishida. Springer Verlag, 2001. p. 45-56 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2252).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Huang, R, Ma, J & Jin, Q 2001, A shopping negotiation agent that adapts to user preferences. in J Liu, PC Yuen, C Li, J Ng & T Ishida (eds), Active Media Technology - 6th International Computer Science Conference, AMT 2001, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2252, Springer Verlag, pp. 45-56, 6th International Computer Science Conference on Active Media Technology, AMT 2001, Hong Kong, China, 01/12/18. https://doi.org/10.1007/3-540-45336-9_9

Huang R, Ma J, Jin Q. A shopping negotiation agent that adapts to user preferences. In Liu J, Yuen PC, Li C, Ng J, Ishida T, editors, Active Media Technology - 6th International Computer Science Conference, AMT 2001, Proceedings. Springer Verlag. 2001. p. 45-56. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-45336-9_9

Huang, Runhe ; Ma, Jianhua ; Jin, Qun. / A shopping negotiation agent that adapts to user preferences. Active Media Technology - 6th International Computer Science Conference, AMT 2001, Proceedings. editor / Jiming Liu ; Pong C. Yuen ; Chun-hung Li ; Joseph Ng ; Toru Ishida. Springer Verlag, 2001. pp. 45-56 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A shopping negotiation agent that adapts to user preferences",

abstract = "This paper describes a shopping negotiation agent that can adapt user preferences and automatically negotiate with its counter party on behalf of a user it represents. The agent is built on a basis of the proposed negotiation model, the enhanced extended Bazaar model, which is a sequence of decision making model of negotiation with exploiting common knowledge, public information, and game theory. Since different users can have different preferences, it is important for the agent to have adaptation to different user preferences. This can be achieved by acquiring user preferences, tracing user{\textquoteright}s behavior on Web and mapping the behavior to a set of the preference parameters, creating the negotiation model class, and generating an instance negotiation model object with new/updated preference parameters.",

author = "Runhe Huang and Jianhua Ma and Qun Jin",

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AB - This paper describes a shopping negotiation agent that can adapt user preferences and automatically negotiate with its counter party on behalf of a user it represents. The agent is built on a basis of the proposed negotiation model, the enhanced extended Bazaar model, which is a sequence of decision making model of negotiation with exploiting common knowledge, public information, and game theory. Since different users can have different preferences, it is important for the agent to have adaptation to different user preferences. This can be achieved by acquiring user preferences, tracing user’s behavior on Web and mapping the behavior to a set of the preference parameters, creating the negotiation model class, and generating an instance negotiation model object with new/updated preference parameters.

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A shopping negotiation agent that adapts to user preferences

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