Abstract
In this paper, we consider a method of computing minimal models in propositional logic. We firstly show that positively minimal disjuncts in DNF (Disjunctive Normal Form) of the original axiom corresponds with minimal models. A disjunct D is positively minimal if there is no disjunct which contains less positive literal than D. We show that using superset query and membership query which were used in some learning algorithms in computational learning theory, we can compute all the minimal models.
We then give a restriction and an extension of the method. The restriction is to consider a class of positive (sometimes called monotone) formula where minimization corresponds with diagnosis and other important problems in computer science. Then, we can replace superset query with sampling to give an approximation method. The algorithm itself has been already proposed by [Valiant84], but we show that the algorithm can be used to approximate a set of minimal models as well.
On the other hand, the extension is to consider circumscription with varied propositions. We show that we can compute equivalent formula of circumscription using a similar technique to the above.
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References
Angluin, D.: Queries and Concept Learning. Machine Learning 2, 319–342 (1988)
Balcázar, J.L., Castro, J.: A New Abstract Combinatorial Dimension for Exact Learning via Queries. Journal of Computer and System Sciences 64, 2–21 (2002)
Ben-Eliyahu, R., Dechter, R.: On Computing Minimal Models. Annals of Mathematics and Artificial Intelligence 18, 3–27 (1996)
Ben-Eliyahu-Zohary, R.: A Demand-Driven Algorithm for Generating Minimal Models. In: Proc. of AAAI-2000, pp. 267–272 (2000)
Blum, A., Jackson, J.C., Sandholm, T., Zinkevich, M.: Preference Elicitation and Query Learning. In: COLT-2003, pp. 13–25 (2003)
Bshouty, N.H., Cleve, R., Gavaldà, Kanna, S., Tamon, C.: Oracles and Queries that are Sufficient for Exact Learning. Journal of Computer and System Sciences 52, 421–433 (1994)
Bshouty, N.H.: Exact Learning Boolean Functions via the Monotone Theory. Information and Computation 123, 146–153 (1995)
Cadoli, M., Schaerf, M.: A Survey on Complexity Results for Non-monotonic Logics. Journal of Logic Programming 17(2-4), 127–160 (1993)
de Kleer, J., Konolige, K.: Eliminating the Fixed Predicates from a Circumscription. Artificial Intelligence 39, 391–398 (1989)
Eiter, T., Gottlob, G.: Identifying the Minimal Transversals of a Hypergraph and Related Problems. SIAM Journal on Computing 24(6), 1278–1304 (1995)
Gunopulos, D., Khardon, R., Mannila, H., Toivonen, H.: Data Mining, Hypergraph Transversals, and Machine Learning. In: Proc. of PODS-1997, pp. 209–216 (1997)
Ibaraki, T., Kameda, T.: A Boolean Theory of Coteries. In: Proc. of 3rd IEEE Symposium on Parallel and Distributed Processing, pp. 150–157 (1991)
Khardon, R., Roth, D.: Reasoning with Models. Artificial Intelligence 87, 187–213 (1996)
Lifschitz, V.: Computing Circumscription. In: Proc. of IJCAI 1985, pp. 121–127 (1985)
McCarthy, J.: Applications of Circumscription to Formalizing Common-Sense Knowledge. Artificial Intelligence 28, 89–116 (1986)
Reiter, R.: A Theory of Diagnosis from First Principles. Artificial Intelligence 38, 49–73 (1987)
Satoh, K., Okamoto, H.: Computing Circumscriptive Databases by Integer Programming: Revisited. In: Proc. of AAAI 2000, pp. 429–435 (2000)
Valiant, L.G.: A Theory of the Learnable. CACM 27, 1134–1142 (1984)
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Satoh, K. (2010). Computing Minimal Models by Positively Minimal Disjuncts. In: Nakakoji, K., Murakami, Y., McCready, E. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2009. Lecture Notes in Computer Science(), vol 6284. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14888-0_28
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DOI: https://doi.org/10.1007/978-3-642-14888-0_28
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