Abstract
Game playing has attracted researchers in Artificial Intelligence ever since its beginnings. By comparison with human reasoning, learning by operationalization of general knowledge, as formalized by the Explanation-Based Generalization (EBG) paradigm, appears to be highly plausible in this domain. Nevertheless, none of the previously published approaches is (provably) sufficient for the target concept, and at the same time applicable to arbitrary game states.
We trace this paradox back to the lack of the expressive means of Negation as Failure in traditional EBG, and constructively support our claim by applying the respective extension proposed in [Schr96] to the chess endgame king-rook vs. king-knight.
Methodically, endgames are well-suited for quantitative evaluation and allow to obtain more rigorous results concerning the effects of learning than in other domains. This is due to the fact that the entire problem space is known (and can be generated) in advance.
We present the main results of a large-scale empirical study. The issues of training complexity, speedup for recognition and classification, as well as the question of optimal reasoning under time constaints are analyzed.
Chapter PDF
References
Clark, K.L., Negation as Failure, in: Gallaire, H., and Minker, J. (eds.), Logic and Data Bases, Plenum Press, NY, (1978), pp 293–322
Fikes, R. E., and Nilsson, N. J., STRIPS: a New Approach to the Application of Theorem Proving to Problem Solving, Artificial Intelligence 2(3–4), pp 189–208
Flann, N.S., and Dietterich, T.G., Selecting Appropriate Representations for Learning from Examples, AAAI-86, (1986), pp 460–466
Flann, N.S., and Dietterich, T.G., A Study of Explanation-Based Methods for Inductive Learning, Machine Learning 4, Kluwer Academic Publishers, Boston, (1989), pp 187–226
Gorlick, M.M., and Kesselman, C.F., Timing Prolog Programs Without Clocks, Symp. on Logic Programming, IEEE Computer Society, (1987), pp 426–432
Kedar-Cabelli, S.T., and McCarty, L.T., Explanation-Based Generalization as Theorem Proving, Proc. of the Fourth International Workshop on Machine Learning, Irvine, (1987)
Keller, R. M., Defining Operationality for Explanation-Based Learning, Artificial Intelligence 35, (1988), pp 227–241
Minton, S., Constraint-Based Generalization: Learning Game-Playing Plans from Single Examples, AAAI-84, (1984), pp 251–254
Minton, S., Quantitative Results Concerning the Utility of Explanation-Based Learning, Artificial Intelligence 42, Elsevier Publishers, (1990), pp 363–391
Mitchell, T., Keller, R., and Kedar-Cabelli, S., Explanation-Based Generalization: A Unifying View, Machine Learning 1:1, (1986), pp 47–80
Puget, J.-F., Explicit Representation of Concept Negation, Machine Learning 14, Kluwer Academic Publishers, Boston, (1994), pp 233–247
Quinlan, J. R., Learning Efficient Classification Procedures and their Application to Chess End Games, in: Michalski, et al. (eds.), Machine Learning: An Artificial Intelligence Approach, San Mateo, CA, Morgan Kaufmann, (1983), pp 463–482
Schrödl, S., Explanation-Based Generalization for Negation as Failure and Multiple Examples, ECAI-96, Budapest, Hungary, John Wiley & Sons, (1996), pp 448–452 (1986)
Siqueira, J. L., and Puget, J. F., Explanation-Based Generalization of Failures, Proc. of ECAI-88, Munich, (1988), pp 339–344
Tadepalli, P., Lazy Explanation-Based Learning: A Solution to The Intractable Theory Problem, IJCAI-89, Detroit MI, (1989), 694–700
Waldinger, R., Achieving Several Goals Simultaneously; in: Elcock, E. W., and Michie, D. (eds.), Machine Intelligence 8, Ellis Horwood, Chichester, England, pp 94-138
Yee, R.C., Saxena, S., Utgoff, P.E., Barto, A.G., Explaining Temporal Differences to Create Useful Concepts for Evaluating States, AAAI-90, (1990), pp 882–888
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Schrödl, S. (1998). Explanation-based generalization in game playing: Quantitative results. In: Nédellec, C., Rouveirol, C. (eds) Machine Learning: ECML-98. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0026696
Download citation
DOI: https://doi.org/10.1007/BFb0026696
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64417-0
Online ISBN: 978-3-540-69781-7
eBook Packages: Springer Book Archive