An intelligent agent for combinatorial auction

Author

Arai, Seiichi ; Miura, Takao

Author_Institution

Dept. of Electr. & Electr. Eng., Hosei Univ., Koganei, Japan

fYear

2011

fDate

5-8 Dec. 2011

Firstpage

34

Lastpage

39

Abstract

In this work, we discuss how an intelligent agent learns in combinatorial auctions. It is well-known that it is NP-complete problem to find the optimal allocation to maximize revenue, because this is a typical form of Set Package Problem (SPP). We apply a framework of machine learning to combinatorial auctions, and discuss how to extract intelligence about bidding behavior. We show empirical convergence of knowledge within Q-learning framework. By this result, we target fully automated negotiation systems.

Keywords

combinatorial mathematics; computational complexity; electronic commerce; learning (artificial intelligence); optimisation; software agents; NP-complete problem; Q-learning framework; automated negotiation system; bidding behavior; combinatorial auction; intelligence extraction; intelligent agent learning; machine learning; optimal allocation; revenue maximization; set package problem; Hard disks; Hybrid intelligent systems; Intelligent agents; Learning; Learning systems; Memory management; Receivers; Auction; Combinatorial Auction; Q-Learning; Reinforcement Learning; e-Commerce;

fLanguage

English

Publisher

ieee

Conference_Titel

Hybrid Intelligent Systems (HIS), 2011 11th International Conference on

Conference_Location

Melacca

Print_ISBN

978-1-4577-2151-9

Type

conf

DOI

10.1109/HIS.2011.6122076

Filename

6122076