E. Lozinskii. Resolving contradictions: a plausible semantics for inconsistent systems. Journal of Automated Reasoning, 12:1-31, 1994.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....Lozinskii s requirements in several cases. The degree of information defined by Lozinskii corresponds to the notion in Shannon s theory, assuming a uniform distribution over the set of propositional interpretations . It is thus required that the input information base # has a classical model. Lozinskii, 1994b] extends [Lozinskii, 1994a] by considering as well some inconsistent logical systems, through a more general notion of model. It is specifically focused on so called quasi models of the information set #, which are the models of the maximal (w.r.t. consistent subsets of #. This is sufficient to ....

E. Lozinskii. Resolving contradictions: a plausible semantics for inconsistent systems. Journal of Automated Reasoning, 12:1--31, 1994.

....is (p and the database contains the facts p and q. In the approach of Lin [78] p q can be inferred (minimal change is captured correctly) but p, q and (p can no longer be inferred (they are all involved in an inconsistency) # Several papers by Lozinskii, Kifer, Arieli and Avron [9,67,81] studied the problem of making inferences from a possibly inconsistent, propositional or first order, knowledge base. The basic idea is to infer the classical consequences of all maximal consistent subsets of the knowledge base [81] or all most consistent models of the knowledge base [9,67] where ....

....# Several papers by Lozinskii, Kifer, Arieli and Avron [9,67,81] studied the problem of making inferences from a possibly inconsistent, propositional or first order, knowledge base. The basic idea is to infer the classical consequences of all maximal consistent subsets of the knowledge base [81] or all most consistent models of the knowledge base [9,67] where the order on models is defined on the basis of atom annotations drawing values from a lattice or a bi lattice) This provides a non monotonic consequence relation but the special role of the integrity constraints (whose truth ....

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E. L. Lozinskii. Resolving Contradictions: A Plausible Semantics for Inconsistent Systems. Journal of Automated Reasoning, 12(1):1--32, 1994.

....is q) and the database contains the facts p and q. In the approach of Lin [60] p q can be inferred (minimal change is captured correctly) but p, q and q) can no longer be inferred (they are all involved in an inconsistency) # Several papers by Lozinski, Kifer, Arieli and Avron [63, 50, 9] studied the problem of making inferences from a possibly inconsistent, propositional or firstorder, knowledge base. The basic idea is to infer the classical consequences of all maximal consistent subsets of the knowledge base [63] or all most consistent models of the knowledge base [50, 9] where ....

....# Several papers by Lozinski, Kifer, Arieli and Avron [63, 50, 9] studied the problem of making inferences from a possibly inconsistent, propositional or firstorder, knowledge base. The basic idea is to infer the classical consequences of all maximal consistent subsets of the knowledge base [63] or all most consistent models of the knowledge base [50, 9] where the order on models is defined on the base of atom annotations drawing values from a lattice or a bi lattice) This provides a non monotonic consequence relation but the special role of the integrity constraints (whose truth ....

[Article contains additional citation context not shown here]

Lozinskii, E.L. Resolving Contradictions: A Plausible Semantics for Inconsistent Systems. Journal of Automated Reasoning, 1994, 12(1):1--32.

....for detecting possible fraud. Inconsistencies may also be useful in cases where they are deployed as directives to guide learning or as indicators for faulty components in a complex system. Hence we need to develop a theoretical framework to distinguish di#erent sorts of inconsistent data. In [14], a definition for measuring the amount of semantic information of an inconsistent set is given. In this section we give a definition for measuring the amount of empirical information in an inconsistent set. By a quasi model of # , we mean any two valued model of any A # M# (# ) Taking # to be ....

E. L. Lozinskii. Resolving contradictions: A plausible semantics for inconsistent systems. Journal of Automated Reasoning, 12:1--31, 1994.

....of Gamma play a role in determining its value. So if U Gamma 6= Gamma) Gamma ) and the addition of any self inconsistent premiss to Gamma would not affect the value. Secondly, the theoretical significance of the function is not well understood. Nonetheless, in a recent article [19], Lozinskii has developed a measurement theory of information that is related to the value of a set. More specifically, Lozinskii is concerned with first order systems with a given finite domain and finite sets of models. He defines the quantity of information of an inconsistent set Gamma as a ....

E. L. Lozinskii. Resolving contradictions: A plausible semantics for inconsistent systems. Journal Of Automated Reasoning, 12:1--31, 1994.

....skeptical reasoning [29, 30] due to which a rational reasoner should believe only statements that are true in all mc subsets. At the other extreme, a credulous reasoner is ready to believe a statement that is true in some mc subset. Another instance of the all mc subsets approach is presented in [21] where the degree of belief in truth of a statement is computed 5 as a certain function of the degrees of belief of the statement sanctioned by every mc subset of the system. These observations raise the following extension of the MAX SAT problem: ALL MC: Given a logic system, find all its ....

....with ambiguous formulas. A formula is ambiguous in S if S contains consistent subsets ff, fi such that ff j= and fi j= Actually, S is inconsistent if and only if there exists a formula ambiguous in S. This inherent ambiguity determines many properties of mc subsets. Theorem 4. 1 [21] (a) A consistent subset oe S is a mc subset of S iff oe j= L for all literals L occurring in oe = S Gamma oe; b) All literals and all clauses occurring in any co mc subset of S are ambiguous in S (however, there may exist a literal ambiguous in S, but not occurring in any co mc subset of ....

E. Lozinskii, Resolving contradictions: A plausible semantics for inconsistent systems. Journal of Automated Reasoning, v. 12, 1994, 1-31.

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E. Lozinskii. Resolving contradictions: a plausible semantics for inconsistent systems. Journal of Automated Reasoning, 12:1-31, 1994.

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E. L. Lozinskii. Resolving Contradictions: a Plausible Semantics for Inconsistent Systems. Journal of Automated Reasoning, 12:1-31, 1994.

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E. L. Lozinskii. Resolving Contradictions: a Plausible Semantics for Inconsistent Systems. Journal of Automated Reasoning, 12:1--31, 1994.

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E. L. Lozinskii. Resolving Contradictions: A Plausible Semantics for Inconsistent Systems. Journal of Automated Reasoning, 12:1-31, 1994.

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E. Lozinskii, Resolving contradictions: a plausible semantics for inconsistent systems. Journal of Automated Reasoning, 12 (1994) 1-31.

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Lozinskii, E. (1994) Resolving contradictions: a plausible semantics for inconsistent systems. Journal of Automated Reasoning,12, 1-31.

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