Abstract
The field of game playing is a particularly well-studied area within the context of AI, leading to the development of powerful techniques, such as the alpha-beta search, capable of achieving competitive game play against an intelligent opponent. It is well known that tree pruning strategies, such as alpha-beta, benefit strongly from proper move ordering, that is, searching the best element first. Inspired by the formerly unrelated field of Adaptive Data Structures (ADSs), we have previously introduced the History-ADS technique, which employs an adaptive list to achieve effective and dynamic move ordering, in a domain independent fashion, and found that it performs well in a wide range of cases. However, previous work did not compare the performance of the History-ADS heuristic to any established move ordering strategy. In an attempt to address this problem, we present here a comparison to two well-known, acclaimed strategies, which operate on a similar philosophy to the History-ADS, the History Heuristic, and the Killer Moves technique. We find that, in a wide range of two-player and multi-player games, at various points in the game’s progression, the History-ADS performs at least as well as these strategies, and, in fact, outperforms them in the majority of cases.
Chancellor’s Professor; Fellow: IEEE and Fellow: IAPR. The second author is also an Adjunct Professor with the Department of ICT, University of Agder, Grimstad, Norway.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
The latter paper won the Best Paper Award of the IEA/AIE conference in 2015.
References
Rimmel, A., Teytaud, O., Lee, C., Yen, S., Wang, M., Tsai, S.: Current frontiers in computer go. IEEE Trans. Comput. Intell. Artif. Intell. Games 2(4), 229–238 (2010)
Russell, S.J., Norvig, P.: Aritificial Intelligence: A Modern Approach, 3rd edn. Prentice-Hall Inc., Upper Saddle River (2009)
Shannon, C.E.: Programming a computer for playing Chess. Phil. Mag. 41, 256–275 (1950)
Baudet, G.M.: An analysis of the full alpha-beta pruning algorithm. In: Proceedings of the Tenth Annual ACM Symposium on Theory of Computing, pp. 296–313 (1978)
Knuth, D.E., Moore, R.W.: An analysis of alpha-beta pruning. Artif. Intell. 6, 293–326 (1975)
Schaeffer, J.: The history heuristic and alpha-beta search enhancements in practice. IEEE Trans. Pattern Anal. Mach. Intell. 11, 1203–1212 (1989)
Akl, S., Newborn, M.: The principal continuation and the killer heuristic. In: Proceedings of ACM 1977 the 1977 Annual Conference, pp. 466–473 (1977)
Sturtevant, N., Games, M.-P.: Algorithms and approaches. Ph.D. thesis, University of California (2003)
Luckhardt, C., Irani, K.: An algorithmic solution of n-person games. In: Proceedings of the AAAI 1986, pp. 158–162 (1986)
Schadd, M.P.D., Winands, M.H.M.: Best Reply Search for multiplayer games. IEEE Trans. Comput. Intell. AI Games 3, 57–66 (2011)
Sturtevant, N., Bowling, M.: Robust game play against unknown opponents. In: Proceedings of AAMAS 2006, The 2006 International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 713–719 (2006)
Sturtevant, N., Zinkevich, M., Bowling, M.,:Prob-Maxn: playing n-player games with opponent models. In: Proceedings of AAAI 2006, 2006 National Conference on Artificial Intelligence, pp. 1057–1063 (2006)
Gonnet, G.H., Munro, J.I., Suwanda, H.: Towards self-organizing linear search. In: Proceedings of FOCS 1979, The 1979 Annual Symposium on Foundations of Computer Science, pp. 169–171 (1979)
Hester, J.H., Hirschberg, D.S.: Self-organizing linear search. ACM Comput. Surv. 17, 285–311 (1985)
Polk, S., Oommen, B.J.: On applying adaptive data structures to multi-player game playing. In: Proceedings of AI 2013, The Thirty-Third SGAI Conference on Artificial Intelligence, pp. 125–138 (2013)
Polk, S., Oommen, B.J.: Novel AI strategies for multi-player games at intermediate board states. In: Proceedings of IEA/AIE 2015, The Twenty-Eighth International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, pp. 33–42 (2015)
Polk, S., Oommen, B.J.: Enhancing history-based move ordering in game playing using adaptive data structures. In: Núñez, M., et al. (eds.) ICCCI 2015. LNCS, vol. 9329, pp. 225–235. Springer, Heidelberg (2015). doi:10.1007/978-3-319-24069-5_21
Polk, S., Oommen, B.J.: Space and depth-related enhancements of the history-ads strategy in game playing. In: Proceedings of CIG 2015, The 2015 IEEE Conference on Computational Intelligence and Games, pp. 322–327 (2015)
Polk, S., Oommen, B.J.: On enhancing recent multi-player game playing strategies using a spectrum of adaptive data structures. In: Proceedings of TAAI 2013, The 2013 Conference on Technologies and Applications of Artificial Intelligence (2013)
Schaeffer, J., Burch, N., Bjornsson, Y., Kishimoto, A., Muller, M., Lake, R., Lu, P., Sutphen, S.: Checkers is solved. Science 14, 1518–1522 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Polk, S., Oommen, B.J. (2016). Challenging Established Move Ordering Strategies with Adaptive Data Structures. In: Fujita, H., Ali, M., Selamat, A., Sasaki, J., Kurematsu, M. (eds) Trends in Applied Knowledge-Based Systems and Data Science. IEA/AIE 2016. Lecture Notes in Computer Science(), vol 9799. Springer, Cham. https://doi.org/10.1007/978-3-319-42007-3_73
Download citation
DOI: https://doi.org/10.1007/978-3-319-42007-3_73
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42006-6
Online ISBN: 978-3-319-42007-3
eBook Packages: Computer ScienceComputer Science (R0)