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Evaluation Function Based Monte-Carlo LOA

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Advances in Computer Games (ACG 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6048))

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Abstract

Recently, Monte-Carlo Tree Search (MCTS) has advanced the field of computer Go substantially. Also in the game of Lines of Action (LOA), which has been dominated so far by αβ, MCTS is making an inroad. In this paper we investigate how to use a positional evaluation function in a Monte-Carlo simulation-based LOA program (MC-LOA). Four different simulation strategies are designed, called Evaluation Cut-Off, Corrective, Greedy, and Mixed. They use an evaluation function in several ways. Experimental results reveal that the Mixed strategy is the best among them. This strategy draws the moves randomly based on their transition probabilities in the first part of a simulation, but selects them based on their evaluation score in the second part of a simulation. Using this simulation strategy the MC-LOA program plays at the same level as the αβ program MIA, the best LOA-playing entity in the world.

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Author information

Authors and Affiliations

  1. Games and AI Group, Department of Knowledge Engineering, Faculty of Humanities and Sciences, Maastricht University, Maastricht, The Netherlands

    Mark H. M. Winands

  2. School of Computer Science, Reykjavík University, Reykjavík, Iceland

    Yngvi Björnsson

Authors
  1. Mark H. M. Winands
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  2. Yngvi Björnsson
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Editor information

Editors and Affiliations

  1. Games and AI Group, MICC, Faculty of Humanities and Sciences, Universiteit Maastricht, Maastricht, The Netherlands

    H. Jaap van den Herik

  2. Institute for Knowledge and Agent Technology, Universiteit Maastricht,  

    Pieter Spronck

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Winands, M.H.M., Björnsson, Y. (2010). Evaluation Function Based Monte-Carlo LOA. In: van den Herik, H.J., Spronck, P. (eds) Advances in Computer Games. ACG 2009. Lecture Notes in Computer Science, vol 6048. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12993-3_4

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  • DOI: https://doi.org/10.1007/978-3-642-12993-3_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-12992-6

  • Online ISBN: 978-3-642-12993-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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