Romanian Academy (RA)

Center for Machine Learning, Natural Language Processing and Conceptual Modeling Bucharest Romania

Researchers Gheorghe Tecuci, Dan Tufis, Stefan Trausan-Matu, Octav Popescu.

ILP Areas Multistrategy learning, Knowledge base revision, Autonomous agents, Integration of machine learning and knowledge acquisition.

Description of Research Over the past several years we have developed several learning methods and systems as, for instance, DISCIPLE, NeoDISCIPLE, and MTL-JT. DISCIPLE, which has been developed in cooperation with LRI of the University of Paris-South, is one of the first multistrategy learning systems [1, 2]. It learns general rules from a human expert by synergistically integrating inductive empirical learning, explanation-based learning and learning by analogy. DISCIPLE has been applied in several domains as, for instance, loudspeaker manufacturing, chemistry, and robotics. NeoDISCIPLE [3, 11] is a development of DISCIPLE which addresses the problem of extending and correcting an imperfect knowledge base. MTL-JT [4, 13] is a general framework for multistrategy-task adaptive learning which dynamically integrates the elementary inferences that are employed by single-strategy learning methods (such as deduction, analogy, abduction, generalization, specialization, abstraction, concretion, etc.), depending of the features of the learning task to be performed.

Current research consists in further developing the MTL-JT framework [13], developing a general methodology for knowledge base refinemet through plausible inference and multistrategy learning [7], and developing autonomous adaptive agents for adversarial planning [9].

Recent Publications

[1] G. Tecuci. DISCIPLE: A theory, methodology and system for learning expert knowledge. PhD Thesis, University of Paris-South, France, 1988.

[2] G. Tecuci, Y. Kodratoff. Apprenticeship learning in imperfect domain theories. In Y. Kodratoff, R. S. Michalski (eds.) Machine Learning: An Artificial Intelligence Approach, Vol. III. Morgan Kaufmann, 1990.

[3] G. Tecuci. Automating knowledge acquisition as extending, updating and improving a knowledge base. IEEE Transactions on Systems, Man and Cybernetics 22 (6), 1444– 1460, 1992.

[4] G. Tecuci. Plausible justification trees: A framework for the deep and dynamic integration of learning strategies. Machine Learning, 11 (2-3), 1993.

[5] R. S. Michalski, G. Tecuci. Multistrategy learning. In A. Kent, J. G. Williams (eds.) Encyclopedia of Microcomputers, Vol. 12. Marcel Dekker, Inc. of New York, 1993.

[6] R. S. Michalski, G. Tecuci (eds.). Proceedings of the Second International Workshop on Multistrategy Learning, Harpers Ferry, West Virginia, May 1993.

[7] G. Tecuci, D. Duff. Integrating multistrategy learning and knowledge acquisition for knowledge base refinement. Proceedings of the IJCAI-93 Workshop on Machine Learning and Knowledge Acquisition, Chambery, France, August 1993.

[8] G. Tecuci, S. Kedar, Y. Kodratoff (eds.). Proceedings of the IJCAI-93 Workshop on Machine Learning and Knowledge Acquisition: Common Issues, Contrasting Methods, and Integrated Approaches, Chambery, France, August 1993.

[9] M. R. Hieb, D. Hille, G. Tecuci. Designing a computer opponent for wargames: Integrating planning, knowledge acquisition and learning in WARGLES. Proceedings of the AAAI 1993 Fall Symposium Series, Raleigh, North Carolina, October 1993.

[10] G. Tecuci (guest editor). Informatica 17 (4), Special Issue on Multistrategy Learning. Slovenia, 1993.

[11] G. Tecuci, M. Hieb. Consistency-driven knowledge elicitation: Using a learningoriented knowledge representation that supports knowledge elicitation in NeoDISCI-PLE. Knowledge Acquisition Journal, December 1993.

[12] R. S. Michalski, G. Tecuci (eds.). Machine Learning: A Multistrategy Approach, Vol. IV. Morgan Kaufmann, San Mateo, 1994.

[13] G. Tecuci. An inference-based framework for multistrategy learning. In R. S. Michalski, G. Tecuci (eds.) Machine Learning: A Multistrategy Approach, Vol. IV. Morgan Kaufmann, San Mateo, 1994.

[14] G. Tecuci, S. Kedar, Y. Kodratoff (guest editors). Knowledge Acquisition Journal, Special Issue on the Integration of Machine Learning and Knowledge Acquisition, June 1994 (to appear).

Contact Person Gheorghe Tecuci Dan Tufis Center for Machine Learning, Natural Language Processing and Conceptual Modeling Calea Victoriei 125 Bucharest 1 Romania Phone: +1 703 993 1722 Fax: +1 703 993 3729 or +40 1 312 0209 E-mail: tecuci@cs.gmu.edu