Abstract
The problem of achieving intelligent game play has been a historically important, widely studied problem within Artificial Intelligence (AI). Although a substantial volume of research has been published, it remains a very active research area with new, innovative techniques introduced regularly. My thesis work operates on the hypothesis that the natural ranking mechanisms provided by the previously unrelated field of Adaptive Data Structures (ADSs) may be able to improve existing game playing strategies. Based on this reasoning, I have examined the applicability of ADSs in a wide range of areas within game playing, leading to the creation of two novel techniques, the Threat-ADS and History-ADS. I have found that ADSs can produce substantial improvements under a broad range of game models and configurations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. pp. 161–201. Prentice-Hall Inc., Upper Saddle River (2009)
Campbell, M.S., Marsland, T.A.: A comparison of minimax tree search algorithms. Artificial Intelligence, 347–367 (1983)
Schaeffer, J.: The history heuristic and alpha-beta search enhancements in practice. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 1203–1212 (1989)
Gelly, S., Wang, Y.: Exploration exploitation in go: UCT for monte-carlo go. In: Proceedings of NIPS 2006, the 2006 Annual Conference on Neural Information Processing Systems (2006)
Szita, I., Chaslot, G., Spronck, P.: Monte-carlo tree search in settlers of catan. In: van den Herik, H.J., Spronck, P. (eds.) ACG 2009. LNCS, vol. 6048, pp. 21–32. Springer, Heidelberg (2010)
Luckhardt, C., Irani, K.: An algorithmic solution of n-person games. In: Proceedings of the AAAI 1986, pp. 158–162 (1986)
Sturtevant, N.: Multi-Player Games: Algorithms and Approaches. PhD thesis, University of California (2003)
Schadd, M.P.D., Winands, M.H.M.: Best Reply Search for multiplayer games. IEEE Transactions on Computational Intelligence and AI in 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)
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 Computing Surveys 17, 285–311 (1985)
Albers, S., Westbrook, J.: Self-organizing data structures. In: Online Algorithms, pp. 13–51 (1998)
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.: 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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Polk, S. (2015). Novel Game Playing Strategies Using Adaptive Data Structures. In: Barbosa, D., Milios, E. (eds) Advances in Artificial Intelligence. Canadian AI 2015. Lecture Notes in Computer Science(), vol 9091. Springer, Cham. https://doi.org/10.1007/978-3-319-18356-5_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-18356-5_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18355-8
Online ISBN: 978-3-319-18356-5
eBook Packages: Computer ScienceComputer Science (R0)