Abstract
For given logical formulae B and E such that B ⊭ E, hypothesis finding means the generation of a formula H such that B ⋀ H ⊨ E. Hypothesis finding constitutes a basic technique for fields of inference, like inductive inference and knowledge discovery. It can also be considered a special case of abduction. In this paper we define a hypothesis finding method which is a combination of residue hypotheses and anti-subsumption. Residue hypotheses have been proposed on the basis of the terminology of the Connection Method, while in this paper we define it in the terminology of resolution. We show that hypothesis finding methods previously proposed on the bases of resolution are embedded into our new method. We also point out that computing residue hypotheses becomes a lot more efficient under the restrictions required by the previous methods to be imposed on hypotheses, but that these methods miss some hypotheses which our method can find. Finally, we show that our method constitutes an extension of Plotkin’s relative subsumption.
In previous works [15],[16] by one of the authors, the bottom method was not well distinguished from inverse entailment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
H. Arimura. Learning Acyclic First-order Horn Sentences From Implication, In Proceedings of the 8th International Workshop on Algorithmic Learning Theory(LNAI 1316), pages 432–445, 1997. 157
W. Bibel. Deduction: Automated Logic. Academic Press, 1993. 157, 159
C.-L. Chang and R. C.-T. Lee. Symbolic Logic and Mechanical Theorem Proving. Academic Press, 1973. 157, 159
B. Fronhöfer and A. Yamamoto. Relevant Hypotheses as a Generalization of the Bottom Method. In Proceedings of the Joint Workshop of SIG-FAI and SIG-KBS, SIG-FAI/KBS-9902, pages 89–96. JSAI, 1999. 157, 159
B. Fronhöfer and A. Yamamoto. Hypothesis Finding with Proof Theoretical Appropriateness Criteria. Submitted to the AI journal, 2000. 157, 159
K. Inoue. Linear Resolution for Consequence Finding. Artificial Intelligence, 56:301–353, 1992. 156
R.C.T. Lee. A Completeness Theorem and Computer Program for Finding Theorems Derivable from Given Axioms. PhD thesis, University of California, Berkeley, 1967. 161
A. Leitsch. The Resolution Calculus. The Resolution Calculus, 1997. 157, 159
S. Muggleton. Inverse Entailment and Progol. New Generation Computing, 13:245–286, 1995. 156
C. H. Papadimitriou. Computational Complexity. Addison Wesley, 1993. 164
G. D. Plotkin. A Further Note on Inductive Generalization. In Machine Intelligence 6, pages 101–124. Edinburgh University Press, 1971. 157, 163
D. Poole. A Logical Framework for Default Reasoning. Artificial Intelligence, 36:27–47, 1988. 156, 163
C. Rouveirol. Extensions of Inversion of Resolution Applied to Theory Completion. In S. Muggleton, editor, Inductive Logic Programming, pages 63–92. Academic Press, 1992. 156
T. Sato and S. Akiba. Inductive Resolution. In Proceedings of the 4th International Workshop on Inductive Logic Programming (LNAI 744), pages 101–110. Springer-Verlag, 1993.
A. Yamamoto. Representing Inductive Inference with SOLD-Resolution. In Proceedings of the IJCAI’97 Workshop on Abduction and Induction in AI, pages 59–63, 1997. 156
A. Yamamoto. Which Hypotheses Can Be Found with Inverse Entailment? In Proceedings of the 7th International Workshop on Inductive Logic Programming (LNAI 1297), pages 296–308, 1997. The extended abstract is in Proceedings of the IJCAI’97 Workshop on Frontiers of Inductive Logic Programming, pp.19–23 (1997). 156, 163
A. Yamamoto. Logical Aspects of Several Bottom-up Fittings. In Proceedings of the 9th International Workshop on Algorithmic Learning Theory (LNAI 1501), pages 158–168, 1998. 163
A. Yamamoto. An Inference Method for the Complete Inverse of Relative Subsumption. New Generation Computing, 17(1):99–117, 1999. 163
A. Yamamoto. Revising the Logical Foundations of Inductive Logic Programming Systems with Ground Reduced Programs. New Generation Computing, 17(1):119–127, 1999. 156
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yamamoto, A., Fronhöfer, B. (2000). Hypotheses Finding via Residue Hypotheses with the Resolution Principle. In: Arimura, H., Jain, S., Sharma, A. (eds) Algorithmic Learning Theory. ALT 2000. Lecture Notes in Computer Science(), vol 1968. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-40992-0_12
Download citation
DOI: https://doi.org/10.1007/3-540-40992-0_12
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41237-3
Online ISBN: 978-3-540-40992-2
eBook Packages: Springer Book Archive