A. Darwiche and P. Marquis. A perspective on knowledge compilation. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI 2001.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....and Kautz [Selman and Kautz, 1996] Beyond this work, there is significant work on approximate reasoning in a logical setting that could be used in terminological reasoning as well. Approximation Techniques Knowledge Compilation In order to avoid complexity at run time, knowledge compilation [Darwiche and Marquis, 2001] aims at explicating knowledge hidden in a logical model in a pre processing step. Derived facts are added to the original theory as axioms avoiding the need to deduct them again. In the case of ontological reasoning, implicit subsumption and membership relations are good candidates for a ....

Darwiche, A. and Marquis, P. (2001). A perspective on knowledge compilation. In Proceedings of the International Joint Conference on Artificial Intelligence IJCAI-01, pages 175--182.

....a detailed study of the complexity of nested circumscription in the first order case and restricted fragments (monadic theories, etc) would be interesting. Complementing the results on reasoning complexity, Cadoli et al. 7, 6] Gogic et al. 26] Selman and Kautz [46] Darwiche and Marquis [14, 13] and others have studied representability issues among KR formalisms, considering problems like representing theories in one KR formalism with polynomial resources in another target formalism, such that the set of models or certain inference relations are preserved. In particular, knowledge ....

A. Darwiche and P. Marquis. A perspective on knowledge compilation. In B. Nebel, editor, Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI-01), pages 175--182. Morgan Kaufmann, 2001.

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A. Darwiche and P. Marquis. A perspective on knowledge compilation. In Proc. of the 17 Joint Conference on Artificial Intelligence (IJCAI '99), pages 175--182, Seattle (WA), 2001.

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Adnan Darwiche and Pierre Marquis. A perspective on knowledge compilation. In Proc. International Joint Conference on Artificial Intelligence (IJCAI), pages 175--182, 2001.

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Adnan Darwiche and Pierre Marquis. A perspective on knowledge compilation. In Proc. International Joint Conference on Artificial Intelligence (IJCAI), pages 175--182, 2001.

....Section 5 is then dedicated to a class of transformations, their applications, and their tractability with respect to the different target compilation languages. We finally close in Section 6 by some concluding remarks. Proofs of theorems are omitted for space limitations but can be found in [Darwiche and Marquis, 2001] . 2 The NNF Language We consider more than a dozen languages in this paper, all of which are subsets of the NNF language, which is defined formally as follows [Darwiche, 1999a; 1999b] Definition 2.1 Let PS be a finite set of prop. variables. A sentence in NNFPS is a rooted, directed acyclic ....

....(model preservation for polysize reductions) exists [Cadoli et al. 1996] but we do not need its full generality here. 6 We stress here that we do not require that there exists a function that computes given in polytime; we only require that a polysize exists. Yet, our proofs in [Darwiche and Marquis, 2001] contain specific algorithms for computing from in certain cases. NNF DNNF CNF d DNNF DNF PI FBDD OBDD IP MODS sd DNNF = Figure 5: An edge L 1 L 2 indicates that L 1 is strictly more succinct than L 2 , L 1 L 2 ; L 1 = L 2 indicates that L 1 and L 2 are equally succinct, L 1 L ....

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Adnan Darwiche and Pierre Marquis. A perspective on knowledge compilation. Technical Report D--116, Cognitive Systems Laboratory, UCLA, Ca 90095, 2001.

....another. Specifically, we show that the network polynomial can be encoded e#ciently using a propositional theory in Conjunctive Normal Form (CNF) We also show that if the CNF is converted to a Negation Normal Form (NNF) that satisfies three properties (smoothness, determinism and decomposability) [6], then we can extract in linear space an arithmetic circuit that computes the encoded polynomial. The main advantage of this new factorization approach is that it allows us to easily exploit local CPT structure and, hence, perform inference on belief networks that are intractable to ....

.... a 1 Figure 3: Extracting an arithmetic circuit from a NNF. 3. 3 Factoring the encoding We show in this section that if the propositional encoding of a multi linear function is compiled into a negation normal form that satisfies three properties (smoothness, determinism, and decomposability) [6, 4], then we can immediately extract from the form an arithmetic circuit that computes the encoded function. A Negation Normal Form (NNF) is a directed acyclic graph in which each internal node is labeled with a conjunction or a disjunction, and each leaf node is labeled with a literal, true, or ....

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Adnan Darwiche and Pierre Marquis. A perspective on knowledge compilation. In Proc. International Joint Conference on Artificial Intelligence (IJCAI), pages 175--182, 2001.

....network can be viewed as a method for constructing such circuits. Moreover, the jointree represents a good data structure for embedding the generated circuits eciently, without having to represent their wires explicitly. A di erent method is proposed, however, for generating such circuits in [2, 3], which relies on encoding the network polynomial as set of propositional sentences and then converting the sentences into a special form (from which a circuit can be extracted immediately) The circuits generated using this method must be represented explicitly, both nodes and wires, but their ....

Adnan Darwiche and Pierre Marquis. A perspective on knowledge compilation. In IJCAI, 2001. To appear.

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A. Darwiche and P. Marquis. A perspective on knowledge compilation. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI 2001.

No context found.

A Darwiche and P Marquis. A perspective on knowledge compilation. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI'01), pages 175--182, 2001.

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