Paper:
Language: English:

A Speedup Algorithm for Repetition of Hypothetical Reasoning

Haruhiko Kimura^, Tadanobu Misawa^, Koji Abe^,
and Yasuhiro Ogoshi^****

^Dept. of Information and System Engineering, Kanazawa University, Kakumamachi, Kanazawa 920-1192, Japan
^School of Management, Tokyo University of Science, 500 Shimokiyoku, Kuki, Saitama 346-8512, Japan
^Dept. of Information and Computer Science, Kanazawa Institute of Technology, 7-1 Ohgigaoka, Nonoichi, Ishikawa 921-8501, Japan
^****Faculty of Engineering, Fukui University, 3-9-1 Bunkyo, Fukui 910-8507, Japan

Received: January 29, 2003

Accepted: November 29, 2005

Keywords: speedup of hypothetical reasoning, accumulated reasoning time, recursive expression

Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.10, No.2 pp. 207-218, 2006

Abstract

This paper presents performance evaluations of a speedup method for hypothetical reasoning to a first-order predicate logic knowledge base in the case when reasoning is executed repeatedly replacing hypothetical knowledge and keeping background knowledge and the goal intact. The proposed method consists of substituting hypotheses for predicate knowledge which has recursive structures, deriving bit patterns of solutions from background knowledge and the goal in advance when all the hypotheses are true, and finding actual solutions from inclusion relation with bit patterns of hypothetical knowledge.

Reference

[1] D. Poole, R. Aleliunas, and R. Goeble, “Theorist: A Logical Reasoning System for Defaults and Diagnosis in the Knowledge Frontier,” Essays in the Knowledge Representation, Springer-Verlag, N. Y., 1987.

[2] B. Selman, and H. J. Levesque, “Abductive and default reasoning,” Proc. AAAI-90, pp. 343-348, 1990.

[3] T. Bylander, D. Allemang, M. C. Tanner, and J. R. Josephson, “The computational complexity of abduction,” Artif. Intell., Vol.49, pp. 25-60, 1991.

[4] T. Either, and G. Gottlob, “The complexity of logic-based abduction,” Journal of the Association for Computing Machinary, Vol.42, No.1-2, pp. 3-42, 1995.

[5] E. Santos Jr., and E. S. Santos, “Polynomial solvability of costbased abduction,” Artif. Intell., Vol.86, pp. 157-170, 1996.

[6] M. Ishizuka, and Y. Matsuo, “SL Method for Computing a Nearoptimal Solution using Linear and Non-linear Programming in Cost-based Hypothetical Reasoning,” Knowledge-based Systems, Vol.15, Issue 7, pp. 369-376, 2002.

[7] J. de Kleer, “An assumption based TMS,” Artif. Intell., Vol.28, pp. 127-162, 1986.

[8] F. Ito, and M. Ishizuka, “A Fast Hypothetical Reasoning System using Inference-Path Network,” Trans. of the Japanese Society for Artificial Intelligence, Vol.6, No.4, pp. 501-509, 1991.

[9] A. Kondo, T. Makino, and M. Ishizuka, “A Fast Hypothetical Reasoning System for Predicate-Logic Knowledge-Base,” Trans. of the Japanese Society for Artificial Intelligence, Vol.8, No.6, pp. 819-827, 1993.

[10] N. Domae, and M. Ishizuka, “Knowledge-Base Reformation for Efficient Hypothetical Reasoning System,” Trans. of the Japanese Society for Artificial Intelligence, Vol.9, No.4, pp. 595-603, 1994.

[11] S. Kato, H. Seki, and H. Itoh, “An Efficient Abductive Reasoning System Based on Program Analysis,” Trans. of the Information Processing Society of Japan, Vol.35, No.10, pp. 2019-2028, 1994.

[12] A. Satter, and R. Goebel, “Consistency-motivated reason maintenance in hypothetical reasoning,” New Generation Computing, Vol.15, pp. 163-186, 1997.

[13] M. Koshino, T. Hayashi, H. Kimura, and S. Hirose, “Speed up of Searching All Solution Hypothetical Reasoning for Predicate-Logic Knowledge-Base,” Trans. of the Japanese Society for Artificial Intelligence, Vol.16, No.2, pp. 202-211, 2001.

[14] H. Nobata, H. Kimura, and S. Hirose, “A Proposal to Reduce Accumulated Reasoning Time on Hypothetical Reasoning,” Trans. of the Institute of Electronics, Information and Communication Engineers D-II, Vol.j80-D-II, No.12, pp. 3164-3172, 1997.

[15] L. Vieille, “Recursive Axioms in Deductive Databases: The Query/Subquery Approach,” Proc. of the 1st Int’l Conf. on Expert Database Systems, pp. 179-193, 1986.