Title :
On learning repeated combinatorial auctions
Author :
Arai, Seiichi ; Miura, Takao
Author_Institution :
Dept. of Electr. & Electr. Eng., Hosei Univ., Tokyo, Japan
Abstract :
In this work, we discuss how an intelligent agent learns in combinatorial auctions. It is well-known that finding the optimal allocation to maximize revenue is NP-complete, because this is a typical form of Set Package Problem (SPP). We introduce a framework of reinforcement learning to combinatorial auctions, and discuss how to obtain intelligence about bidding behavior. We show empirical convergence of knowledge within Q-learning framework. By this result, we target fully automated negotiation systems.
Keywords :
computational complexity; electronic commerce; learning (artificial intelligence); software agents; NP-complete problem; Q-learning framework; automated negotiation system; bidding behavior; combinatorial auction; intelligent agent; optimal allocation; reinforcement learning; set package problem; Arrays; Convergence; Hard disks; Intelligent agents; Learning; Learning systems; Receivers; Auction; Combinatorial Auction; Q-Learning; Reinforcement Learning; e-Commerce;
Conference_Titel :
Communications, Computers and Signal Processing (PacRim), 2011 IEEE Pacific Rim Conference on
Conference_Location :
Victoria, BC
Print_ISBN :
978-1-4577-0252-5
Electronic_ISBN :
1555-5798
DOI :
10.1109/PACRIM.2011.6032900
