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Multi-agent Learning for Winner Determination in Combinatorial Auctions

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Book cover Modern Advances in Applied Intelligence (IEA/AIE 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8481))

Abstract

The winner determination problem (WDP) in combinatorial double auctions suffers from computation complexity. In this paper, we attempt to solve the WDP in combinatorial double auctions based on an agent learning approach. Instead of finding the exact solution, we will set up a fictitious market based on multi-agent system architecture and develop a multi-agent learning algorithm to determine the winning bids in the fictitious market to reduce the computational complexity in solving the WDP in combinatorial double auctions. In the fictitious market, each buyer and each seller is represented by an agent. There is a mediator agent that represents the mediator. The issue is to develop learning algorithms for all the agents in the system to collectively solve the winner determination problem for combinatorial double auctions. In this paper, we adopt a Lagrangian relaxation approach to developing efficient multi-agent learning algorithm for solving the WDP in combinatorial double auctions. Numerical results indicate our agent learning approach is more efficient than the centralized approach.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Chaoyang University of Technology, 41349, Taichung, Taiwan

    Fu-Shiung Hsieh & Chi-Shiang Liao

Authors
  1. Fu-Shiung Hsieh
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  2. Chi-Shiang Liao
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Texas State University, TX 78666, San Marcos, USA

    Moonis Ali

  2. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, No. 415, Chien Kung Road, 80778, Kaohsiung, Taiwan

    Jeng-Shyang Pan

  3. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

  4. Department of Electronics Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan (ROC)

    Mong-Fong Horng

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© 2014 Springer International Publishing Switzerland

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Hsieh, FS., Liao, CS. (2014). Multi-agent Learning for Winner Determination in Combinatorial Auctions. In: Ali, M., Pan, JS., Chen, SM., Horng, MF. (eds) Modern Advances in Applied Intelligence. IEA/AIE 2014. Lecture Notes in Computer Science(), vol 8481. Springer, Cham. https://doi.org/10.1007/978-3-319-07455-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-07455-9_1

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07454-2

  • Online ISBN: 978-3-319-07455-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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