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Combinatorial Auctions, Knapsack Problems, and Hill-Climbing Search

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Advances in Artificial Intelligence (Canadian AI 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2056))

Abstract

This paper examines the performance of hill-climbing algorithms on standard test problems for combinatorial auctions (CAs). On single-unit CAs, deterministic hill-climbers are found to perform well, and their performance can be improved significantly by randomizing them and restarting them several times, or by using them collectively. For some problems this good performance is shown to be no better than chancel; on others it is due to a well-chosen scoring function. The paper draws attention to the fact that multi-unit CAs have been studied widely under a different name: multidimensional knapsack problems (MDKP). On standard test problems for MDKP, one of the deterministic hill-climbers generates solutions that are on average 99% of the best known solutions.

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Author information

Authors and Affiliations

  1. School of Information Technology and Engineering, University of Ottawa, Ottawa, Ontario, Canada, K1N 6N5

    Robert C. Holte

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  1. Robert C. Holte
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Editors and Affiliations

  1. Department of Computer Science, University of Alberta, Edmonton, AB, Canada, T6G 2E8

    Eleni Stroulia

  2. School of Information Technology and Engineering, University of Ottawa, Ottawa, ON, Canada, K1N 6N5

    Stan Matwin

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© 2001 Springer-Verlag Berlin Heidelberg

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Holte, R.C. (2001). Combinatorial Auctions, Knapsack Problems, and Hill-Climbing Search. In: Stroulia, E., Matwin, S. (eds) Advances in Artificial Intelligence. Canadian AI 2001. Lecture Notes in Computer Science(), vol 2056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45153-6_6

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  • DOI: https://doi.org/10.1007/3-540-45153-6_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42144-3

  • Online ISBN: 978-3-540-45153-2

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