Abstract
This paper describes a representation framework that offers a unifying platform for alternative systems, which learn concepts in First Order Logics. The main aspects of this framework are discussed. First of all, the separation between the hypothesis logical language (a version of the VL21 language) and the representation of data by means of a relational database is motivated. Then, the functional layer between data and hypotheses, which makes the data accessible by the logical level through a set of abstract properties is described. A novelty, in the hypothesis representation language, is the introduction of the construct of internal disjunction; such a construct, first used by the AQ and Induce systems, is here made operational via a set of algorithms, capable to learn it, for both the discrete and the continuous-valued attributes case. These algorithms are embedded in learning systems (SMART+, REGAL, SNAP, WHY, RTL) using different paradigms (symbolic, genetic or connectionist), thus realizing an effective integration among them; in fact, categorical and numerical attributes can be handled in a uniform way. In order to exemplify the effectiveness of the representation framework and of the multistrategy integration, the results obtained by the above systems in some application domains are summarized.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Ade, H., De Raedt, L., & Bruynooghe, M. (1995). Declarative bias for specific-to-general ILP systems. Machine Learning, 20:119–154.
Apt, K., Blair, H., & Walker, A. (1988). Towards a theory of declarative knowledge. In Minker, J., editor, Foundations of Deductive Databases and Logic Programming, pages 89–148. Morgan Kaufmann, Los Altos, CA.
Bala, J., De Jong, K., & Pachowicz, P. (1991). Learning noise tolerant classification procedures by integrating inductive learning and genetic algorithms. In First International Workshop on Multistrategy Learning, pages 316–323, Harpers Ferry, WV.
Baroglio, C. & Botta, M. (1995). Multiple predicate learning with RTL. In 4th Congress of the Italian Association for Artificial Intelligence AI*IA'95, LNAI-992, pages 44–55, Florence, Italy.
Baroglio, C., Botta, M., & Saitta, L. (1994). WHY: A system that learns using causal models and examples. In Michalski, R. and Tecuci, G., editors, Machine Learning: A Multistrategy Approach, volume IV, pages 319–347. Morgan Kaufmann, San Francisco, CA.
Baroglio, C., Giordana, A., Kaiser, M., Nuttin, M., & Piola, R. (1996). Learning controllers for industrial robots. Machine Learning, 23:221–250.
Bäck, T. (1995). Generalized convergence models for tournament-and (μ, λ)-selection. In 6th International Conference on Genetic Algorithms, pages 2–8, Pittsburg, PA.
Berenji, H. (1992). An architecture for designing fuzzy controllers with neural networks. International Journal of Approximate Reasoning, 6(2):267–292.
Bergadano, F. & G iordana, A. (1988). A knowledge intensive approach to concept induction. In Proceedings of the 5th International Conference on Machine Learning, pages 305–317, Ann Arbor, MI. Morgan Kauffman.
Bergadano, F., Giordana, A., & Saitta, L. (1988). Learning concepts in noisy environment. IEEE Transaction on Pattern Analysis and Machine Intelligence, PAMI-10:555–578.
Bergadano, F., Giordana, A., & Saitta, L. (1991). Machine Learning: An Integrated Framework and its Applications. Hellis Horwood, Chichester, UK.
Blanzieri, E. & Katenkamp, P. (1996). Learning radial basis function networks on-line. In 13th International Conference on Machine Learning, pages 37–45, Bari, Italy.
Botta, M. (1994). Learning first order theories. In 8th International Symposium on Methodologies for Intelligent Systems, LNAI-869, pages 356–365, Charlotte, NC.
Botta, M. & Giordana, A. (1993). SMART+: A multi-strategy learning tool. In IJCAI-93, Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pages 937–943, Chambéry, France.
Botta, M. & Giordana, A. (1996). Combining symbolic and numeric methods for learning to predict temporal series. In Proceedings of the 3rd International Workshop on Multistrategy Learning, pages 234–249, Harpers Ferry, WV.
Botta, M., Saitta, L., Brancadori, F., De Marchi, D., & Radicchi, S. (1992). Automatic construction of second generation diagnostic expert systems. International Journal of Expert Systems, 4:389–400.
Bratko, I. & Džeroski, S. (1995). Engineering applications of ILP. New Generation Computing, 13:313–333.
Brockhausen, P. & Morik, K. (1996). Direct access of an ILP algorithm to a database management system. In Proc. of the MLnet Familiarization Workshop, pages 95–110, Bari, Italy.
Buntine, W. (1988). Generalized subsumption and its application to induction and redundancy. Artificial Intelligence, 36:149–176.
Cameron-Jones, R. & Quinlan, R. (1993). Avoiding pitfalls when learning recursive theories. In IJCAI-93, Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pages 1050–1055, Chambéry, France.
Clark, K. (1978). Negation as failure. In Gallaire, H. and Minker, J., editors, Logic and Data Bases, pages 293–322. Plenum Press, New York, NY.
De Jong, K. A., Spears, W. M., & Gordon, F. D. (1993). Using genetic algorithms for concept learning. Machine Learning, 13:161–188.
De Raedt, L. & Džeroski, S. (1994). First-order jk-clausal theories are PAC-learnable. Artificial Intelligence, 70:375–392.
DeJong, G. & Mooney, R. (1986). Explanation based learning: an alternative view. Machine Learning, 1:145–176.
Dietterich, T. & Michalski, R. (1983). A comparative review of selected methods for learning from examples. In Carbonell, J., Michalski, R., and Mitchell, T., editors, Machine Learning, an Artificial Intelligence Approach, pages 41–82. Morgan Kaufmann, Los Altos, CA.
Faure, C., Frediani, S., & Saitta, L. (1993). A semiautomated methodology for knowledge elicitation. IEEE Transaction on Systems, Man, and Cybernetics, SMC-23:346–356.
Flach, P. (1995). Conjectures: An Inquiry Concerning the Logic of Induction. PhD thesis, Tilburg University (Netherlands), CIP-Gegevens KoninKlijke Bibliotheek, Den Haag.
Gardin, G. & Simon, E. (1987). Les systèmes de gestion de bases des données déductives. Synthèse, 6.
Gemello, R., Mana, F., & Saitta, L. (1991). Rigel: An inductive learning system. Machine Learning, 6:7–36.
Giordana, A. & Neri, F. (1996). Search-intensive concept induction. Evolutionary Computation, 3:375–416.
Giordana, A. & Saitta, L. (1990). Abstraction: a general framework for learning. In Working Notes of the AGAA-90 Workshop, pages 245–256, Boston, MA.
Giordana, A. & Saitta, L. (1993). REGAL: an integrated system for learning relations using genetic algorithms. In Proceedings of the 2nd International Workshop on Multistrategy Learning, pages 234–249, Harpers Ferry, WV.
Giordana, A. & Saitta, L. (1994). Learning disjunctive concepts by means of genetic algorithms. In Proceedings of the 11th International Conference on Machine Learning, pages 96–104, New Brunswick, NJ.
Giordana, A., Saitta, L., & Baroglio, C. (1993a). Learning simple recursive theories. In Methodologies for Intelligent Systems, Proc. of the 7th International Symposium, ISMIS-93, pages 425–434, Trondheim, Norway. Springer-Verlag.
Giordana, A., Saitta, L., Bergadano, F., Braucadon, F., & De Marchi, D. (1993b). Enigma: a system that learns diagnostic knowledge. IEEE Transactions on Knowledge and Data Engineering, 5(1):15–28.
Giordana, A., Saitta, L., & Roverso, D. (1991). Abstracting concepts with inverse resolution. In Proc. of 8th International Workshop on Machine Learning, pages 142–146, Evanston, IL.
Giordana, A. & Sale, C. (1992). Genetic algorithms for learning relations. In Proc. of 9th International Conference on Machine Learning, pages 169–178, Aberdeen, UK.
Goldberg, D. (1989). Genetic Algorithms. Addison-Wesley.
Gordon, D. & desJardins, M. (1995). Evaluation and selection of biases in machine learning. Machine Learning, 20:5–22.
Greene, D. & Smith, S. (1993). Competition-based induction of decision models from examples. Machine Learning, 13:229–258.
Grefenstette, J., Ramsey, C., & Schultz, A. (1990). Learning sequential decision rules using simulation models and competition. Machine Learning, 5:355–381.
Haussler, D. (1988). Learning conjunctive concepts in structural domains. Machine Learning, 4:7–40.
Hayes-Roth, F. & McDermott, J. (1978). An interference matching technique for inducing abstractions. Communications of the ACM, 21:401–411.
Helft, N. (1989). Induction as nonmonotonic inference. In Proc. of 1st Conference on Knowledge Representation and Reasoning, pages 149–156, Boston, MA.
Henschen, L. J. & Naqvi, S. A. (1984). On compiling queries in recursive, first order databases. Journal of ACM, 31, pages 47–85.
Janikow, C. (1993). A knowledge intensive genetic algorithm for supervised learning. Machine Learning, 13:198–228.
Kodratoff, Y. & Ganascia, J. (1986). Improving the generalization step in learning. In Carbonell, J.G., Michalski, R., and Mitchell, T. M., editors, Machine Learning, an Artificial Intelligence Approach, volume II, pages 215–244. Morgan Kaufmann, San Mateo, CA.
Lavra?, N., Džeroski, S., & Grobelnik, M. (1991). Learning nonrecursive definitions of relations with LINUS. In Proceedings of the 5th European Working Session on Learning, pages 265–281, Porto, Portugal.
McCallum, R. & Spackman, K. (1990). Using genetic algorithm to learn disjunctive rules from examples. In Proc. of the 7th International Conference on Machine Learning, pages 149–152, Austin, Texas.
Michalski, R. (1980). Pattern recognition as a rule-guided inductive inference. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-2:349–361.
Michalski, R. (1983). A theory and methodology of inductive learning. In Michalski, R., Carbonell, J., and Mitchell, T., editors, Machine Learning: An Artificial Intelligence Approach, pages 83–134, Los Altos, CA. Morgan Kaufmann.
Michalski, R. (1991). Inferential learning theory as a basis for multistrategy task-adaptive learning. In Proc of 1st International Workshop on Multistrategy Learning, pages 3–18, Harpers Ferry, WV.
Michalski, R. & Chilausky, R. (1980). Learning by being told and learning from examples: an experimental comparison of the two methods of knowledge acquisition in the context of developing an expert system for soybean disease diagnosis. International Journal of Policy Analysis and Information Systems, 4:125–126.
Minker, J., editor (1988). Foundations of Deductive Databases and Logic Programming. Morgan Kaufmann, Los Altos, CA.
Mitchell, T., Keller, R., & Kedar-Cabelli, S. (1986). Explanation based generalization: a unifying view. Machine Learning, 1:47–80.
Moody, J. & Darken, C. (1988). Learning with localized receptive fields. In Sejnowski, T., Touretzky, D., and Hinton, G., editors, Connectionist Models Summer School, Carnegie Mellon University.
Morik, K. (1991). Balanced cooperative modeling. In Proc. of the 1st International Workshop on Multistrategy Learning, pages 65–80, Harpers Ferry, WV.
Muggleton, S. (1991). Inductive logic programming. New Generation Computing, 8:295–318.
Muggleton, S. (1995). Inverse entailment and Progol. New Generation Computing, 13:245–286.
Muggleton, S. & Feng, C. (1990). Efficient induction of logic programs. In Proc. of the 1st Conference on Algorithmic Learning Theory, pages 368–381, Japan.
Neri, F. (1997). First order Logic Concept Learning by means of a Distributed Genetic Algorithms. Phd. Thesis, University of Torino, Italy, available at http://www.di.unito.it.ct/~neri/phd/thesis.ps.gz
Neri, F. & Saitta, L. (1996). Exploring the power of genetic search in learning symbolic classifiers. IEEE Trans. on Pattern Analysis and Machine Intelligence, PAMI-18:1135–1142.
Opitz, D. & Shavlik, J. (1993). Heuristically expanding knowledge-based neural networks. In Proceedings of the 13th International Joint Conference on Artificial Intelligence, pages 1360–1365, Chambéry, France.
Opitz, D. & Shavlik, J. (1995). Dynamically adding symbolically meaningful nodes to knowledge-based neural networks. Knowledge Based Systems, 8:301–311.
Pagallo, G. (1989). Learning DNF by decision trees. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, pages 639–644, Detroit, MI.
Pazzani, M. & Kibler, D. (1992). The utility of knowledge in inductive learning. Machine Learning, 9:57–94.
Plotkin, G. (1970). A note in inductive generalization. In Meltzer, B. and Michie, D., editors, Machine Intelligence, volume V, pages 153–163.
Poggio, T. & Girosi, F. (1990). Networks for approximation and learning. Proceedings of the IEEE, 78(9):1481–1497.
Pompe, U. & Kononenko, I. (1995). Linear space induction in first order logic with Relief. In ISSEKWorkshop, volume CISM Lecture Notes. Springer-Verlag.
Quinlan, R. (1983). Efficient classification procedures. In Carbonell, J., Michalski, R., and Mitchell, T., editors, Machine Learning, an Artificial Intelligence Approach, pages 463–482. Morgan Kaufmann, Los Altos, CA.
Quinlan, R. (1990). Learning logical definitions from relations. Machine Learning, 5:239–266.
Rumelhart, D., Hinton, G., & Williams, R. (1985). Learning internal representations by error propagation. Technical Report 8506, Institute for Cognitive Science, La Jolla: University of California, San Diego.
Rumelhart, D. E. & McClelland, J. L. (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Parts I & II. MIT Press, Cambridge, Massachusetts.
Saitta, L., Botta, M., & Neri, F. (1993). Multistrategy learning and theory revision. Machine Learning, 11:153–172.
Saitta, L., Giordana, A., & Neri, F. (1995). What is the “Real World”? In Proc. of Workshop on Applying Machine Learning in Practice, pages 34–40, Lake Tahoe, CA.
Sammut, C., Hurst, S., Kedzier, D., & Michie, D. (1992). Learning to fly. In Proceedings of the Ninth International Conference on Machine Learning (ML92), pages 385–393. Morgan Kaufmann.
Shapiro, E. (1983). Algorithmic Program Debugging. The MIT Press, Cambridge, MA.
Silverstein, G. & Pazzani, M. (1991). Relational cliches: Constraining constructive induction during relational learning. In Proc. of the 8th International Workshop on Machine Learning, pages 203–207, Evanston, IL.
Towell, G., Shavlik, J., & Noordwier, M. (1990). Refinement of approximate domain theories by knowledge-based neural networks. In Proceedings of the 8th National Conference on Artificial Intelligence AAAI'90, pages 861–866.
Tresp, V., Hollatz, J., & Ahmad, S. (1993). Network structuring and training using rule-based knowledge. In Advances in Neural Information Processing Systems 5 (NIPS-5).
Ullman, J. (1982). Principles of Databases. Computer Science, Baltimore, MD.
Vafaie, H. & De Jong, K. (1991). Improving the performance of rule induction system using genetic algorithms. In Proc. of 1st International Workshop on Multistrategy Learning, pages 305–315, Harpers Ferry, WV.
Vere, S. (1978). Inductive learning of relational production. In Pattern-Directed Inference System, pages 281–295. Academic Press, London, UK.
Vieille, L. (1986). Recursive axioms in deductive databases: the query subquery approach. In Proc. of First International Conference on Expert Database Systems, Charleston, SC.
Winston, P. (1975). Learning structural descriptions from example. In Winston, P., editor, The psychology of computer vision, pages 157–209. McGraw Hill, New York, NY.
Wnek, J. & Michalski, R. (1994). Hypothesis-driven constructive induction in AQ17-HCI: A method and experiments. Machine Learning, 14(2):139–168.
Zadeh, L. (1965). Fuzzy sets. Information Control, 8:338–353.
Zadeh, L. (1992). Knowledge representation in fuzzy logic. In Yager, R. and Zadeh, L., editors, An Introduction to Fuzzy Logic Applications in Intelligent Systems. Kluver Academic Publishers.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Giordana, A., Neri, F., Saitta, L. et al. Integrating Multiple Learning Strategies in First Order Logics. Machine Learning 27, 209–240 (1997). https://doi.org/10.1023/A:1007361708126
Issue Date:
DOI: https://doi.org/10.1023/A:1007361708126