Here is a list of references used in this blog:
BOOKS
Bergadano, F. and Gunetti, D. (1996). Inductive Logic Programming: From Machine Learning to Software Engineering. MIT Press. Cambridge, Massachusetts.
Bramer, M.A. (ed.)(1983). Computer Game Playing: Theory and Practice. Chichester, West Sussex : E. Horwood. Halsted Press.
Bratko, I. (1990), Prolog: Programming for Artificial Intelligence. 2nd ed. Addison-Wesley: Wokingham, England.
Covington, M., Nute, D., Vellino, A. (1997), Prolog Programming In Depth. Prentice Hall: Upper Saddle River, New Jersey.
Devore, J.L. (1991). Probability and Statistics for Engineering and the Sciences. 3rd ed. Pacific Grove, California: Brooks/Cole.
Hayes, J.E., Michie, D., Pao, Y. (eds.) (1982). Machine Intelligence 10. Ellis Horwood, West Sussex.
Lavrac, N. and Dzeroski, S. (1994), Inductive Logic Programming: Techniques and Applictions, Ellis Horwood, New York. Available at http://www-ai.ijs.si/SasoDzeroski/ILPBook/.
Luger, G. F. and Stubblefield, W.A. (1993), Artificial Intelligence: Structure and Strategies for Complex Problem Solving, 2nd ed. Benjamin/Cummings Redwood City CA.
Nilsson, U. and Maaluszynski, J. (2000), Programming, Logic and Prolog, 2nd ed. John Wiley and Sons. Available at http://www.ida.liu.se/~ulfni/lpp.
Michalski, Carbonell, Mitchell (eds.). (1983). Machine Learning: An Aritificial Intelligence Approach. Kaufmann-Morgan, Redwood CA.
Mitchell, T.M. 1997. Machine Learning. McGraw-Hill.
Norvig, P. (1992), Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp, Morgan Kaufmann San Francisco CA.
Quinlan, J.R. (1992). C4.5 Programs for Machine Learning, San Mateo, CA: Morgan Kaufmann.
R. Poli, W. B. Langdon, and N. F. McPhee. A field guide to genetic programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk/, 2008. (With contributions by J. R. Koza).
Russell, S. and Norvig, P. (1995). Artificial Intelligence: A Modern Approach. Englewood Cliffs, NJ: Prentice Hall.
Sterling, L. and Shapiro, E. (1994). The Art of Prolog: Advance Programming Techniques. 2nd ed. MIT Press: Cambridge, Massachusetts.
Winston, P.H. and Horn, B.K.P. (1989) LISP, 3rd Ed. Addison-Welsey Reading MA.
ARTICLES
Dzeroski, L., De Raedt, L., and Blockeel, H. (1998). Relational Reinforcement Learning. Shavlik, J. et. al (eds) In the Proceedings of the International Conference in Machine Learning (ICML 98). Morgan Kaufmann.
Ganzfried, S. (2004). A Simple Algorithm for Knight’s Tours. REU Report. Oregon State University.
Hauptman, A. and Sipper, M. GP-endchess: Using genetic programming to evolve chess endgame players. In M. Keijzer, et al., editors, Proceedings of the 8th European Conference on Genetic Programming, volume 3447 of Lecture Notes in Computer Science, pages 120–131, Lausanne, Switzerland, 30 March – 1 April 2005. Springer. ISBN 3-540-25436-6.
Hauptman, A. and Sipper, M. Evolution of an efficient search algorithm for the mate-in-N problem in chess. In M. Ebner, et al., editors, Proceedings of the 10th European Conference on Genetic Programming, volume 4445 of Lecture Notes in Computer Science, pages 78–89, Valencia, Spain, 11 – 13 April 2007. Springer. ISBN 3-540-71602-5.
Morales, E. Scaling Up Reinforcement Learning with a Relational Representation. In the Proceedings of the Workshop on Adaptability in Multiagent Systems. pp. 15-26, 2003.
Morales, E. PAL: A pattern-based first-order inductive system. Machine Learning 26: 227-252, 1997.
Morales, E. On learning how to play. Advances in Computer Chess 8, H.J. van den Herik y J.W.H.M. Uiterwijk (eds), Universiteit Maastricht, The Netherlands, pp. 235-250, 1997.
Morales, E. Learning Playing Strategies in Chess. Computational Intelligence , Vol. 12 (1): 65-87, 1996.
Morales,E. Learning Patterns for Playing Strategies. ICCA Journal, Vol. 17 (1): 15–26, 1994.
Pohl, I. (1967). A Method For Finding Hamiltonian Paths and Knight Tours. Technical Report SLAC-PUB-261. Stanford University.
Pohl, I., Stockmeyer, L. (2004). Pohl-Warnsdorf Revisited. Technical Report. University of California, Santa Cruz.
Quinlan, J.R. (1983). Learning efficient classification procedures and their application to chess endgames. In Mikalski, Carbonell, and Mitchell.
Robinson, J.A. (1965), A machine-oriented logic based on the resolution principle Communications of the ACM, Vol. 5 pp 23–41.
Simon, H. (1983). Why Should Machines Learn? in Machine Learning: An Aritificial Intelligence Approach. Michalski, Carbonell, Mitchell (eds.). Kaufmann-Morgan, Redwood CA, 1983, 25-38.
Stearn, D.; Herbrich, R.; Graepel, T. (2007). Learning to Solve Game Trees. In the Proceedings of International Conference in Machine Learining (ICML 2007).
Tenenbaum, J.B. (1999), Bayesian Modeling of Human Concept Learning in Advances in Neural Information Processing Systems 11. Kearns, M., Solla, S., and Cohn, D. (eds). Cambridge, MIT Press, 1999, 59-65.
Utgoff, P.E., Berkman, N.C., Clouse, J.A. 1997. Decision Tree Induction based on Efficient Tree Restructuring. Machine Learning, 29, 5-24.
Utgoff, P.E. 1989. Incremental induction of decision trees. Machine Learning, 4, 161-186.
Wilkins, D.E. (1979), Using Plans in Chess in the Proceedings of the International Joint Conference of Artificial Intelligence.
