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Abstract: Knowledge compilation speeds inference by creating tractable approximations of a knowledge base, but this advantage is lost if the approximations are too large. We show how learning concept generalizations can allow for a more compact representation of the tractable theory. We also give a general induction rule for generating such concept generalizations. Finally, we prove that unless NP ` non-uniform P, not all theories have small Horn least upper-bound approximations. 1 Introduction Work in... (Update)
Context of citations to this paper: More
...to pure DATALOG axioms) to PDDL preserving plan size polynomially. Proof Sketch. We use a proof idea similar to the one Kautz and Selman [13] used to prove that approximations of logical theories of a certain size are not very likely to exist. By using a DATALOG theory in...
...for such unconstrained compilation schemes changes nothing. In order to prove this, we will use an idea similar to the one Kautz and Selman [1992] used to prove that approximations of logical theories of a certain size are not very likely to exist. In order to do so, we...
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0.1: Learning an Optimally Accurate Representation System - Greiner, Schuurmans (1994) (Correct)
0.1: Knowledge Compilation Using Horn Approximations - Selman, Kautz (1991) (Correct)
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0.3: Encoding Plans in Propositional Logic - Kautz, McAllester, Selman (1996) (Correct)
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BibTeX entry: (Update)
H. A. Kautz and B. Selman. Forming concepts for fast inference. In Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI-92), pages 786-- 793, 1992. http://citeseer.ist.psu.edu/kautz92forming.html More
@inproceedings{ kautz92forming,
author = "Henry A. Kautz and Bart Selman",
title = "Forming Concepts for Fast Inference",
booktitle = "{ECAI} Workshop on Knowledge Representation and Reasoning",
pages = "200-215",
year = "1992",
url = "citeseer.ist.psu.edu/kautz92forming.html" }
Citations (may not include all citations):534 The complexity of theorem-proving procedures (context) - Cook - 1971
191 Linear time algorithms for testing the satisfiability of pro.. (context) - Dowling, Gallier - 1984
181 Mathematical Logic (context) - Shoenfield - 1967
154 Machine invention of first-order predicates by inverting res.. (context) - Muggleton, Buntine - 1988
147 Boolean feature discovery in empirical learning (context) - Pagallo, Haussler - 1990
141 The complexity of finite functions (context) - Boppana, Sipser - 1990
97 Knowledge compilation using horn approximations - Selman, Kautz - 1991
90 Learning Effective Search Control Knowledge: an ExplanationB.. (context) - Minton - 1988
85 Turing machines that take advice (context) - Karp, Lipton - 1982
52 The Computational Complexity of Machine Learning (context) - Kearns - 1990
41 On representations of problems of reasoning about actions (context) - Amarel - 1968
40 Selectively generalizing plans for problem solving (context) - Minton - 1985
32 The relevance of irrelevance (context) - Subramanian, Genesereth - 1987
16 A general framework for knowledge compilation - Kautz, Selman - 1991
8 Learning near-optimal horn approximations (context) - Greiner - 1992
1 Reappr - improving planning efficience via local expertise a.. (context) - Bresina, Marsella et al. - 1986
The graph only includes citing articles where the year of publication is known.
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