Reasoning Algorithm for a Creative Decision Support System Integrating Inference and Machine Learning

Dorota Wilk-Kolodziejczyk


In this paper a reasoning algorithm for a creative decision support system is proposed. It allows to integrate inference and machine learning algorithms. Execution of learning algorithm is automatic because it is formalized as aplying a complex inference rule, which generates intrinsically new knowledge using the facts stored already in the knowledge base as training data. This new knowledge may be used in the same inference chain to derive a decision. Such a solution makes the reasoning process more creative and allows to continue resoning in cases when the knowledge base does not have appropriate knowledge explicit encoded. In the paper appropriate knowledge representation and infeence model are proposed. Experimental verification is performed on a decision support system in a casting domain.


reasoning algorithm; inferential theory of learning; decision support; rule induction; logic of plausible reasoning

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Alkharouf N.W., Michalski R.S.: Multistrategy Task-adaptive Learning Using Dynamically Interlaced Hierarchies. In: W.J. Michalski R. S., ed., Proceedings of the Third International Workshop on Multistrategy Learning. 1996.

Bach K., Deutsch J.O., Hanft A., Manz J., Muller T., Newo R., Reichle M., Schaaf M., Weis K.H., Althor K.D.: Collaborative multi-expert-systems. In: Proceedings of the International Conference on Artifcial Intelligence. 2007.

Boehm-Davis D., Dontas K., Michalski R.S.: Plausible Reasoning: An Outline of Theory and the Validation of its Structural Properties. North Holland, 1990.

Boehm-Davis D., Dontas K., Michalski R.S.: A Validation and Exploration of the Collins-Michalski Theory of Plausible Reasoning. Reports of the Machine Learning and Inference Laboratory. George Mason University, 1990.

C. E.A., Wiriyacoonkasem S.: Adaptive learning expert systems. In: Proceedings of the IEEE, Southeastcon. 2000.

Collins A., Michalski R.S.: The Logic of Plausible Reasoning: A Core Theory. In: Cognitive Science, vol. 13, pp. 1–49, 1989.

Gabbay D.M.: LDS – Labeled Deductive Systems. Oxford University Press, 1991.

Hancock J.P., Tran L.P.: An adaptive-learning expert system for maintenance diagnostics. In: Proceedings of the National Aerospace and Electronics Conference. 1989.

Hart P., Nilsson N., Raphae J.B., Hart P.: A Formal Basis for the Heuristic Determination of Minimum Cost Path. In: Trans. Syst. Science and Cybernetics, vol. 4, pp. 100–107, 1968.

Hieb M.R., Michalski R.S.: A Knowledge Representation System Based on Dynamically Interlaced Hierarchies: Basic Ideas and Examples. Reports of the Machine Learning and Inference Laboratory. George Mason University, 1993.

Hieb M.R., Michalski R.S.: Multitype Inference in Multistrategy Task-Adaptive Learning: Dynamic Interlaced Hierarchies. Reports of the Machine Learning and Inference Laboratory. George Mason University, 1993.

Ho Chung L., Ah Hwee T., Hoon Heng T., Boon Toh L.: Connectionist expert system with adaptive learning capability. In: Knowledge and Data Engineering, vol. 3, pp. 200–207, 1991.

Kowalski R.: Logic for Problem Solving. Oxford, New York, 2002.

Larson J., Michalski R.S.: Aqval/1 (aq7) user’s guide and program description. University of Illinois, Urbana, 1975.

Ligeza A.: Logical Foundations for Rule-Based Systems. Springer Berlin Heidelberg, 2nd edition, New York, 2006.

Michalski R.S.: Inferential Theory of Learning: Developing Foundations for Multistrategy Learning. Morgan Kaufmann Publishers, 1994.

Morgan C.G.: Autologic. In: Logique et Analyse, vol. 28, pp. 257–282, 1985.

Neapolitan R.E.: Probabilistic Reasoning in Expert Systems: Theory and Algorithms. CreateSpace Independent Publishing Platform, USA, 2012.

Quinlan J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, USA, 1993.

Riley G.: Clips - an expert system building tool. CA, San Jose, 2001.

Sniezynski B.: Integration of inference and machine learning as a tool for creative reasoning. Modeling Changing Perspectives - Reconceptualizing Sensorimotor Experiences. Physica-Verlag, Springer.

Sniezynski B.: Probabilistic Label Algebra for the Logic of Plausible Reasoning. In: M. Klopotek, S. Wierzchon, M. Michalewicz, eds., Intelligent Information Systems, Advances in Soft Computing. Physica-Verlag, Springer, 2002.

Sniezynski B.: Proof Searching Algorithm for the Logic of Plausible Reasoning. In: M. Klopotek, ed., Intelligent Information Processing and Web Mining, Advances in Soft Computing, pp. 393–398. Springer, 2003.

Sniezynski B.: Recommendation System Using Multistrategy Inference and Learning. Advances in Soft Computing. Physica-Verlag, Springer, 2005.

Zadeh L.A.: Fuzzy sets. In: Information and Control, vol. 8, pp. 338–353, 1965.



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