Abstract
A general game player is a program that is able to play arbitrary games well given only their rules. One of the main problems of general game playing is the automatic construction of a good evaluation function for these games. Distance features are an important aspect of such an evaluation function, measuring, e.g., the distance of a pawn towards the promotion rank in chess or the distance between Pac-Man and the ghosts.
However, current distance features for General Game Playing are often based on too specific detection patterns to be generally applicable, and they often apply a uniform Manhattan distance regardless of the move patterns of the objects involved. In addition, the existing distance features do not provide proven bounds on the actual distances.
In this paper, we present a method to automatically construct distance heuristics directly from the rules of an arbitrary game. The presented method is not limited to specific game structures, such as Cartesian boards, but applicable to all structures in a game. Constructing the distance heuristics from the game rules ensures that the construction does not depend on the size of the state space, but only on the size of the game description which is exponentially smaller in general. Furthermore, we prove that the constructed distance heuristics are admissible, i.e., provide proven lower bounds on the actual distances.
We demonstrate the effectiveness of our approach by integrating the distance heuristics in an evaluation function of a general game player and comparing the performance with a state-of-the-art player.
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 subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bonet, B., Geffner, H.: Planning as heuristic search. Artificial Intelligence 129(1-2), 5–33 (2001)
Clune, J.: Heuristic evaluation functions for general game playing. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1134–1139. AAAI Press (2007)
Hoffmann, J., Nebel, B.: The FF planning system: Fast plan generation through heuristic search. JAIR 14, 253–302 (2001)
Kaiser, D.M.: Automatic feature extraction for autonomous general game playing agents. In: Proceedings of the Sixth Intl. Joint Conf. on Autonomous Agents and Multiagent Systems (2007)
Kissmann, P., Edelkamp, S.: Instantiating General Games Using Prolog or Dependency Graphs. In: Dillmann, R., Beyerer, J., Hanebeck, U.D., Schultz, T. (eds.) KI 2010. LNCS, vol. 6359, pp. 255–262. Springer, Heidelberg (2010)
Kuhlmann, G., Dresner, K., Stone, P.: Automatic Heuristic Construction in a Complete General Game Player. In: Proceedings of the Twenty-First National Conference on Artificial Intelligence, pp. 1457–1462. AAAI Press, Boston (2006)
Love, N., Hinrichs, T., Haley, D., Schkufza, E., Genesereth, M.: General game playing: Game description language specification. Tech. Rep., Stanford University (March 4, 2008), the most recent version should be available at http://games.stanford.edu/
Pell, B.: Strategy generation and evaluation for meta-game playing. Ph.D. thesis, University of Cambridge (1993)
Schiffel, S., Thielscher, M.: Fluxplayer: A successful general game player. In: Proceedings of the National Conference on Artificial Intelligence, pp. 1191–1196. AAAI Press, Vancouver (2007)
Schiffel, S., Thielscher, M.: Automated theorem proving for general game playing. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) (2009)
Schiffel, S., Thielscher, M.: A Multiagent Semantics for the Game Description Language. In: Filipe, J., Fred, A., Sharp, B. (eds.) ICAART 2009. CCIS, vol. 67, pp. 44–55. Springer, Heidelberg (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Michulke, D., Schiffel, S. (2013). Admissible Distance Heuristics for General Games. In: Filipe, J., Fred, A. (eds) Agents and Artificial Intelligence. ICAART 2012. Communications in Computer and Information Science, vol 358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36907-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-36907-0_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36906-3
Online ISBN: 978-3-642-36907-0
eBook Packages: Computer ScienceComputer Science (R0)