H. Katsuno and A. O. Mendelzon. Propositional knowledge base revision and minimal change. Arti cial Intelligence, 52(3):263-294, 1991.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....distance of each possible model of the merged requirements from each agent s position. Using this measure, we can identify an optimal choise in terms of minimizing the sum of the distances for each agent to that choice. This can be seem as adaptation of approaches such as by Katsumo and Mendelzon [KM91] 6.2 Expectations on merges in practice We now consider how the framework fits into wider technology solutions, and consider the expectations this raises on knowledgebase merging. We start with considering software engineering, and then distributed multi agent systems. The development of most ....

H Katsuno and A Mendelzon. Propositional knowledge base revision and minimal change. Artificial Intelligence, 52:263--294, 1991.

....C = B # A, we have B # (A (B # A) B # A) That is directly B # (B # A) B # A. Theorem 8. Mem 2 ) Mem 3 ) etc. cannot be derived from the AGM postulates. Proof. This can be established e.g. by considering Dalal s revision operator [5] which is known to satisfy the AGM postulates [12] and showing that is does not satisfy the (Mem i ) postulates. Indeed, consider B = p, A 1 = q, A 2 = p q. Then B 2 = p # q) p q) p q where is the exclusive or. But this is di#erent from B # B 2 = # ( p # q) # ( p q) q) p # q. We can ....

H. Katsuno and A. O. Mendelzon. Propositional knowledge base revision and minimal change. Artificial Intelligence, 52:263--294, 1991.

....subjective assessment of their relative plausibility, with the worlds in [K] being the most plausible, i.e. minimal. Given the input sentence b, the agent then takes as his new belief set the set of sentences true in all the most plausible worlds satisfying b. Precisely we have: Theorem I ([13]) Let K be a belief set and an operator which, for each L c, returns a new belief set K, Then is a simple AGM revision function for K iff there exists some total pre order on W, anchored on [K] such that, for all L c, K Th(min( b] In this paper we will make extensive use of the ....

H. Katsuno and A. O. Mendelzon, Propositional knowledge base revision and minimal change, Artificial Intelligence 52 (1991) 263-294.

.... a = K (I( 7) a) n ) a (K S) If K (a ) then K (a ) There are a number of different methods for con structing AGM contraction operations. We briefly describe a model theoretic semantic method which uses total preorders on V. From results by Grove [7] and Katsuno and Mendelzon [11], AGM contraction can be characterised semantically by a set of total preorders (i.e. connected, reflexive, transitive relations) on V. For any total preorder on V, we say that x W V is minimal in W iff for every y W, x y, and we denote the set of minimal elements of M(a) by M (a) ....

.... A GM contraction can be defined in terms of a K faithful total preorder using (Der from 3 An infatom semantics for belief change The model theoretic semantics for AGM contrac tion described in section 2 is frequently viewed as a suitable representation of the epistemic states of an agent [11], 3] But if we think of the elements of an epistemic state as objects from which (linguistic) beliefs are built up, valuations do not seem to be appropriate basic building blocks. For it is difficult to see how a valuation can be considered as a basic part of a belief expressed as a sentence in ....

H. Katsuno and A.O. Mendelzon. Proposi- tional knowledge base revision and minimal change. Artificial Intelligence, 52:263-294, 1991.

....that is undergoing the revision, and that the domain theory is nonempty. On the other hand, the research on belief revision is typically limited to the propositional case. Our implicit notion of revision satisfies the postulates (R1) R5) R7) and (R8) introduced by Katsuno and Mendelzon [66]. Dalal [32] postulated a di#erent notion of revision, based on minimizing the cardinality of the set of changes, as opposed to minimizing the set of changes under set inclusion [3,96] In [6] it is shown how to capture repairs under Dalal s notion of revision by means of logic programs for ....

H. Katsuno and A. O. Mendelzon. Propositional Knowledge Base Revision and Minimal Change. Artificial Intelligence, 52(3):263--294, 1992.

....time, an intelligent agent faces incomplete, uncertain or inaccurate information. The arrival of a new item of information, more reliable or more cer tain leads the agent to refine (specify) his beliefs, to revise them. Belief revision is a well known problem in Artificial intelligence [1] 9] [13], in this context, an epistemic state encodes a set of beliefs about the real world (based on the available information) An epistemic state is generally interpreted as a plausibility ordering between possible states of the world, or as a preference relation between information sources from which ....

H. Katsuno and A. Mendelzon. Propositional Knowledge Base Revision and Minimal Change. Artificial Intelligence, 52:263-294, 1991.

....pre orders on interpretations. In [ Konieczny and Pino P erez, 2001; Konieczny and Pino P erez, 2000 ] we de ne a family of revision operators that we have called revision operators with memory. Those operators can be de ned from any classical AGM revision operator [ Alchourr on et al. 1985; Katsuno and Mendelzon, 1991 ] and they have good properties for iterated revision. In fact revision operators with memory use the faithful assignment provided by the classical AGM revision operator as an a priori information. This a priori information is attached to the new evidence, and the completed information obtained ....

.... current epistemic state represented by a pre order over possible worlds by a new piece of information a formula is the epistemic state (pre order) obtained after the following two steps: First, take the pre order associated to by the AGM revision operator (faithful assignment [ Katsuno and Mendelzon, 1991 ] given at the beginning of the process. Second, take the lexicographical pre order associ The Dalal distance [Dalal, 1988] is a Hamming distance between interpretations. ated to and . The pre order obtained in this way is the new epistemic state. Note that there is a very static ....

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H. Katsuno and A. O. Mendelzon. Propositional knowledge base revision and minimal change. Arti cial Intelligence, 52:263-294, 1991.

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H. Katsuno and A. O. Mendelzon. Propositional knowledge base revision and minimal change. Arti cial Intelligence, 52(3):263-294, 1991.

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Katsuno, H., and Mendelzon, A. O. 1991. Propositional knowledge base revision and minimal change. Artificial Intelligence 52(3):263--294.

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Katsuno, H., and Mendelzon, A. O. 1991. Propositional knowledge base revision and minimal change. Artificial Intelligence 52(3):263--294.

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H. Katsuno and A. Mendelzon. Propositional knowledge base revision and minimal change. Artificial Intelligence, 52:263--294, 1991.

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H. Katsuno and A. Mendelzon. Propositional knowledge base revision and minimal change. Artificial Intelligence, 52(3):263--294, 1991. 40

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H. Katsuno and A.O. Mendelzon. Propositional knowledge base revision and minimal change. Artificial Intelligence, 52:263--294, 1991.

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H. Katsuno and A. Mendelzon. Propositional knowledge base revision and minimal change. Artificial Intelligence, 52:263--294, 1991.

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H. Katsuno and A. O. Mendelzon. Propositional knowledge base revision and minimal change. Articial Intelligence, 52:263294, 1991.

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H. Katsuno and A. O. Mendelzon. Propositional knowledge base revision and minimal change. Articial Intelligence, 52:263294, 1991.

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Katsuno, H. and Mendelzon, A. O. (1991) Propositional knowledge base revision and minimal change. Art. Intell., 52, 263--294.

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H. Katsuno and A. Mendelzon. Propositional knowledge base revision and minimal change. Artificial Intelligence, 52(3):263--294, 1991.

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H. Katsuno, A. Mendelzon. Propositional Knowledge Base Revision and Minimal Change. Technical Report KRR-TR-90-3, Technical Reports on Knowledge Representation and Reasoning, Univ. of Toronto, 1990.

No context found.

Katsuno and A.O. Mendelzon, `Propositional knowledge base revision and minimal change', Artificial Intelligence, 52, (1991) 263-294.

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