NDLI: Scalable multi-agent learning algorithms to determine winners in combinatorial double auctions

Content Provider	Springer Nature Link
Author	Hsieh, Fu Shiung Liao, Chi Shiang
Copyright Year	2015
Abstract	Many complex decision problems in the real world are very difficult to solve due to computational complexity. The winner determination problem (WDP) in combinatorial double auctions is one example of such complex problems. Although it allows buyers and sellers to submit bids and trade goods conveniently, the WDP in combinatorial double auctions is notoriously difficult to solve from computation point of view. It relies on the development of an effective mechanism to determine the winners. Multi-agent systems (MAS) provide an approach in which several agents attempt, through their interactions, to jointly solve a problem. An important issue in MAS is the design of multi-agent learning algorithms. In this paper, we will study the development of scalable multi-agent learning algorithms for solving the WDP in combinatorial double auctions. Instead of finding the exact solution, we will set up a fictitious market based on MAS architecture and develop multi-agent learning algorithms to reduce the computational complexity in solving the WDP of combinatorial double auctions. In the fictitious market, each buyer, each seller and the mediator is modeled by an agent. The issue is to develop learning algorithms for all the agents in the system to collectively solve the WDP in combinatorial double auctions. In this paper, we adopt a Lagrangian relaxation approach and a subgradient method to develop efficient multi-agent learning algorithms for solving the WDP in combinatorial double auctions. The effectiveness of the proposed multi-agent learning algorithms is also demonstrated by numerical examples.
Starting Page	308
Ending Page	324
Page Count	17
File Format	PDF
ISSN	0924669X
Journal	Applied Intelligence
Volume Number	43
Issue Number	2
e-ISSN	15737497
Language	English
Publisher	Springer US
Publisher Date	2015-02-25
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Auction Multi-agent Learning Artificial Intelligence (incl. Robotics) Mechanical Engineering Manufacturing, Machines, Tools
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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