A. Herzig and O. Ri . Propositional belief base update and minimal change. Arti cial Intelligence, 115(1):107-138, 1999. Home/Search Document Details and Download
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A Knowledge Compilation Map - Darwiche, Marquis (2002) (2 citations) (Correct)
....forgetting variables. In model based diagnosis, compiling away every variable except the abnormality ones does not remove any piece of information required to compute the conflicts and the diagnoses of a system [10] Forgetting has also been used to design update operators with valuable properties [18]. Proposition 5.1 The results in Table 3 hold. One can draw a number of observations regarding Table 3. First, all languages we consider satisfy CD and, hence, lend themselves to efficient application of the conditioning transformation. As for forgetting multiple variables, only DNNF, DNF, PI ....
A. Herzig and O. Rifi. Propositional belief base update and minimal change. Artificial Intelligence, 115(1):107-- 138, 1999.
A Perspective on Knowledge Compilation - Darwiche, Marquis (2001) (4 citations) (Correct)
.... In model based diagnosis, compiling away every variable except the abnormality ones does not remove any piece of information required to compute the conflicts and the diagnoses of a system [Darwiche, 1999b] Forgetting has also been used to design update operators with valuable properties [Herzig and Rifi, 1999] . Proposition 5.1 The results in Table 3 hold. One can draw a number of observations regarding Table 3. First, all languages we consider satisfy CD and, hence, lend themselves to efficient application of the conditioning transformation. As for forgetting multiple variables, only DNNF, DNF, PI ....
A. Herzig and O. Rifi. Propositional belief base update and minimal change. Artificial Intelligence, 115(1):107--138, 1999.
Relevance Sensitive Belief Structures - Chopra, Parikh (2000) (Correct)
....revision was N , the new parameter size is k l p where n; k; l N . This means that in the rst case, we have a search space of size 2 N and in the second case we have a search space of size 2 k l p . Such a reduction indicates considerable savings on computational costs. Herzig and Ri [20] have shown that the problem of calculating the smallest language for a formula is in co NP in the size of the formula. However, since we think of an existing belief structure as being large and any particular incoming formula as being relatively short, a problem which is NP complete in the ....
Andreas Herzig and Omar Ri. Propositional belief base update and minimal change. Articial Intelligence, 115(1):107-138, 1999.
Relevance Sensitive Non-Monotonic Inference on Belief.. - Chopra, Georgatos, Parikh (2000) (1 citation) (Correct)
....simplest equivalent form is most likely, computationally non trivial. Complexity of the Inference Procedure In the methods de ned above, there are two sources of complexity. For any query the rst one involves the calculation of the smallest language L , which is a co NP complete problem [HR99], but only in the (relatively short) length of the individual formula and not in the size of the entire belief base . The second one involves checking the consistency of the set i at each step of the construction of the set of the maxiconsistent set related to the query formula . However, for ....
Andreas Herzig and Omar Ri. Propositional belief base update and minimal change. Articial Intelligence, 115(1):107-138, 1999.
Relevance Sensitive Non-Monotonic Inference on Belief.. - Chopra, Georgatos, Parikh (2000) (1 citation) (Correct)
....simplest equivalent form is most likely, computationally non trivial. 10 3.3.2 Complexity of the Inference Procedure Note that in the methods de ned above, there are two sources of complexity. The rst one involves the calculation of the smallest language which is a co NP complete problem ([HR99]) and the second one involves the complexity of checking the consistency of the set i at each step of the construction of the set . Consider the length of a sequence to be n. Then the answering method is dependent upon rst calculating the relevance relation exhaustively for the entire sequence. ....
Andreas Herzig and Omar Ri. Propositional belief base update and minimal change. In Articial Intelligence, 1999, to appear. 13
Elaborating Domain Descriptions - Andreas (2006) Self-citation (Herzig) (Correct)
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A. Herzig and O. Ri . Propositional belief base update and minimal change. Arti cial Intelligence, 115(1):107-138, 1999.
Beliefs, Intentions, Speech Acts and Topics - Herzig, Longin (2000) Self-citation (Herzig) (Correct)
....the system lacks information to decide whether the incoming information corresponds to real world change or not. 1 In our example, after u 2 the system might be unable to distinguish between the case where the user has changed his mind and the case where he has misunderstood utterance u 1 . See [29] for a detailed critique of the KM framework. Moreover, both revision and update have several common properties that are not suitable in dialogues. In particular, the over informing nature of some information is neglected, expressed by the postulate (K ffi A) K if K A. Finally, no ....
Andreas Herzig and Omar Rifi. Propositional belief base update and minimal change. Artificial Intelligence Journal, 115(1):107--138, December 1999.
Conservative Belief Change: A Gricean Approach - James Delgrande School (Correct)
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A. Herzig and O. Rifi. Propositional belief base update and minimal change. Artificial Intelligence, 115:107--138, 1999.
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