Abstract
The game of chess has been a major testbed for research in artificial intelligence, since it requires focus on intelligent reasoning. Particularly, several challenges arise to machine learning systems when inducing a model describing legal moves of the chess, including the collection of the examples, the learning of a model correctly representing the official rules of the game, covering all the branches and restrictions of the correct moves, and the comprehensibility of such a model. Besides, the game of chess has inspired the creation of numerous variants, ranging from faster to more challenging or to regional versions of the game. The question arises if it is possible to take advantage of an initial classifier of chess as a starting point to obtain classifiers for the different variants. We approach this problem as an instance of theory revision from examples. The initial classifier of chess is inspired by a FOL theory approved by a chess expert and the examples are defined as sequences of moves within a game. Starting from a standard revision system, we argue that abduction and negation are also required to best address this problem. Experimental results show the effectiveness of our approach.
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
Burg, D.B., Just, T.: IUS Chess Federation Official Rules of Chess. McKay, New York (1987)
Caruana, R.: Multitask Learning. Machine Learning 28(1), 41–75 (1997)
Chan, D.: Constructive Negation Based on the Completed Database. In: Proc. of the 5th Int. Conf. and Symp. on Logic Programming, pp. 111–125. The MIT Press, Cambridge (1988)
Drabent, W.: What is Failure? An Approach to Constructive Negation. Acta Inf. 32(1), 27–29 (1995)
Duboc, A.L., Paes, A., Zaverucha, G.: Using the Bottom Clause and Mode Declarations in FOL Theory Revision from Examples. Machine Learning 76, 73–107 (2009)
Flach, P., Kakas, A.: Abduction and Induction: Essays on their Relation and Integration. Kluwer Academic Publishers, Dordrecht (2000)
FĂ¼rnkranz, J.: Recent Advances in Machine Learning and Game Playing. OGAI-Journal 26(2), 147–161 (2007)
Goodacre, J.: Master thesis, Inductive Learning of Chess Rules Using Progol. Programming Research Group, Oxford University (1996)
Muggleton, S.: Inverse Entailment and Progol. New Generation Computing 13(3&4), 245–286 (1995)
Muggleton, S., Bryant, C.H.: Theory completion using inverse entailment. In: Cussens, J., Frisch, A.M. (eds.) ILP 2000. LNCS (LNAI), vol. 1866, pp. 130–146. Springer, Heidelberg (2000)
Muggleton, S., De Raedt, L.: Inductive Logic Programming: Theory and Methods. J. Log. Program. 19/20, 629–679 (1994)
Paes, A., Zaverucha, G., Costa, V.S.: Revising FOL Theories from Examples through Stochastic Local Search. In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli, P. (eds.) ILP 2007. LNCS (LNAI), vol. 4894, pp. 200–210. Springer, Heidelberg (2007)
Pritchard, D.B.: The Classified Encyclopedia of Chess Variants. John Beasley (2007)
Richards, B.L., Mooney, R.J.: Automated Refinement of First-order Horn-Clause Domain Theories. Machine Learning 19(2), 95–131 (1995)
Thrun, S.: Is Learning the nth Thing any Easier than Learning the First? In: Adv. in Neural Inf. Proc. Systems. NIPS, vol. 8, pp. 640–646. MIT Press, Cambridge (1995)
Wrobel, S.: First-order theory refinement. In: De Raedt, L. (ed.) Advances in Inductive Logic Programming, pp. 14–33. IOS Press, Amsterdam (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Muggleton, S., Paes, A., Santos Costa, V., Zaverucha, G. (2010). Chess Revision: Acquiring the Rules of Chess Variants through FOL Theory Revision from Examples. In: De Raedt, L. (eds) Inductive Logic Programming. ILP 2009. Lecture Notes in Computer Science(), vol 5989. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13840-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-13840-9_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13839-3
Online ISBN: 978-3-642-13840-9
eBook Packages: Computer ScienceComputer Science (R0)