Abstract
Non-learning problem solvers have been applied to many interesting and complex domains. Experience-based learning techniques have been developed to augment the capabilities of certain non-learning problem solvers in order to improve overall performance. An alternative approach to enhancing pre-existing systems is automatic case elicitation, a learning technique in which a case-based reasoning system with no prior domain knowledge acquires knowledge automatically through real-time exploration and interaction with its environment. In empirical testing in the domain of checkers, results suggest not only that experience can substitute for the inclusion of pre-coded model-based knowledge, but also that the ability to explore is crucial to the performance of automatic case elicitation.
This is a preview of subscription content, log in via an institution to check access.
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
Schaeffer, J.: One Jump Ahead: Challenging Human Supremacy in Checkers. Springer, Heidelberg (1997)
Beal, D.F.: A generalised quiescence search algorithm. Artificial Intelligence 43, 85–98 (1990)
Grefenstette, J.J., Ramsey, C.L.: An approach to anytime learning. In: Proceedings of the Ninth International Machine Learning Workshop, San Mateo, CA, pp. 189–195. Morgan Kaufmann, San Francisco (1992)
DeJong, K.A., Shultz, A.C.: Using experience-based learning in game-playing. In: Proceedings of the Fifth International Conference on Machine Learning, San Mateo, California, pp. 284–290. Morgan Kaufmann, San Francisco (1988)
Powell, J.H., Hauff, B.M., Hastings, J.D.: Utilizing case-based reasoning and automatic case elicitation to develop a self-taught knowledgeable agent. In: Fu, D., Orkin, J. (eds.) Challenges in Game Artificial Intelligence: Papers from the AAAI Workshop (Technical Report WS-04-04), pp. 77–81. AAAI Press, Menlo Park (2004)
Kaelbling, L.P., Littman, M.L., Moore, A.P.: Reinforcement learning: A survey. Journal of Artificial Intelligence Research 4, 237–285 (1996)
Smyth, B., Keane, M.T.: Remembering to forget: A competence-preserving case deletion policy for case-based reasoning systems. In: Proceedings of the 14th International Conference on Artificial Intelligence (IJCAI 1995), Montreal, Canada, pp. 377–382 (1995)
Watanabe, H., Okuda, K., Fukiwara, S.: A strategy for forgetting cases by restricting memory. IEICE Transactions on Information and Systems, 1324–1326 (1995)
Mukkamalla, S., Muñoz-Avila, H.: Case acquisition in a project planning environment. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 264–277. Springer, Heidelberg (2002)
Flinter, S., Keane, M.T.: On the automatic generation of case libraries by chunking chess games. In: Aamodt, A., Veloso, M.M. (eds.) ICCBR 1995. LNCS, vol. 1010, pp. 421–430. Springer, Heidelberg (1995)
Sinclair, D.: Using example-based reasoning for selective move generation in two player adversarial games. In: Smyth, B., Cunningham, P. (eds.) EWCBR 1998. LNCS (LNAI), vol. 1488, pp. 126–135. Springer, Heidelberg (1998)
Shih, J.: Sequential instance-based learning for planning in the context of an imperfect information game. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 483–501. Springer, Heidelberg (2001)
Fagan, M., Cunningham, P.: Case-based plan recognition in computer games. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, pp. 161–170. Springer, Heidelberg (2003)
Wendler, J., Lenz, M.: CBR for dynamic situation assessment in an agent-oriented setting. In: Aha, D.W., Daniels, J.J. (eds.) Case-Based Reasoning Integrations: Papers from the AAAI Workshop (Technical Report WS-98-15), Madison, WI. AAAI Press, Menlo Park (1998)
Fasciano, M.J.: Real-time case-based reasoning in a complex world. Technical Report TR-96-05, Computer Science Department, University of Chicago (1996)
Goodman, M.: Projective visualization: Acting from experience. In: Proceedings of the Eleventh National Conference on Artificial Intelligence (AAAI 1993), Menlo Park, Calif., pp. 54–59. AAAI Press, Menlo Park (1993)
Goodman, M.: Results on controlling action with projective visualization. In: Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI 1994), Menlo Park, Calif., pp. 1245–1250. AAAI Press, Menlo Park (1994)
Samuel, A.L.: Some studies in machine learning using the game of checkers. IBM Journal on Reseach and Developement 3, 211–229 (1959)
Samuel, A.L.: Some studies in machine learning using the game of checkers, ii – recent progress. IBM Journal on Reseach and Developement 11, 601–617 (1967)
Likhachev, M., Kaess, M., Arkin, R.C.: Learning behavioral parameterization using spatio-temporal case-based reasoning. In: Proceedings of the 2002 IEEE International Conference on Robotics and Automation, vol. 2, pp. 1282–1289 (2002)
Kira, Z., Arkin, R.C.: Forgetting bad behavior: Memory management for case-based navigation. In: Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3145–3152 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Powell, J.H., Hauff, B.M., Hastings, J.D. (2005). Evaluating the Effectiveness of Exploration and Accumulated Experience in Automatic Case Elicitation. In: Muñoz-Ávila, H., Ricci, F. (eds) Case-Based Reasoning Research and Development. ICCBR 2005. Lecture Notes in Computer Science(), vol 3620. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536406_31
Download citation
DOI: https://doi.org/10.1007/11536406_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28174-0
Online ISBN: 978-3-540-31855-2
eBook Packages: Computer ScienceComputer Science (R0)