By Maria Vanina Martinez, Visit Amazon's Cristian Molinaro Page, search results, Learn about Author Central, Cristian Molinaro, , V.S. Subrahmanian, Leila Amgoud
This SpringerBrief proposes a basic framework for reasoning approximately inconsistency in a wide selection of logics, together with inconsistency solution equipment that experience now not but been studied. The proposed framework permits clients to specify personal tastes on the best way to unravel inconsistency whilst there are a number of how one can accomplish that. This empowers clients to unravel inconsistency in facts leveraging either their specified wisdom of the knowledge in addition to their software wishes. The short indicates that the framework is well-suited to deal with inconsistency in different logics, and offers algorithms to compute most well-liked strategies. ultimately, the short exhibits that the framework not just captures numerous present works, but additionally helps reasoning approximately inconsistency in numerous logics for which no such equipment exist today.
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Extra resources for A General Framework for Reasoning On Inconsistency
In: International conference on scalable uncertainty management (SUM), Marburg, pp 58–71 Amgoud L, Besnard P (2010) A formal analysis of logic-based argumentation systems. In: International conference on scalable uncertainty management (SUM), Toulouse, pp 42–55 Amgoud L, Cayrol C (2002) Inferring from inconsistency in preference-based argumentation frameworks. J Autom Reason 29(2):125–169 Artale A (2008) Formal methods: linear temporal logic. pdf Bacchus F (1990) Representing and reasoning with probabilistic knowledge.
Proof. Straightforward. The inclusion-based preferred subbases are of the form (X1 ∪ . . t. (X1 ∪ . . ∪ Xi ) is a maximal (under set inclusion) consistent subbase of (K1 ∪ . . n. 2 above, which can be expressed in our framework. Finally, the lexicographic preferred subbases are of the form (X1 ∪ . . t. (X1 ∪ . . ∪ Xi ) is a cardinality-maximal consistent subbase of (K1 ∪ . . n; we denote them by Plex (K ). 8. Consider a knowledge base K and let W⊆ be the adopted weakening mechanism. t. O1 = CN(K1 ).
T. O = CN(KS ). t. sc(O ) < sc(O). t. O = CN(KS ). Since the objective function of LP(K ) corresponds to sc, then S does not minimize the objective function, which is a contradiction. 2. Suppose that O ∈ Opt P (K , WP ). t. O = CN(KS ). Suppose by contradiction that S is not a solution of LP(K ). This means that it does not minimize the objective function. Then, there is a solution S of LP(K ) which has a lower value of the objective function. As shown before, O = CN(KS ) is an option and has a score lower than O, which is a contradiction.
A General Framework for Reasoning On Inconsistency by Maria Vanina Martinez, Visit Amazon's Cristian Molinaro Page, search results, Learn about Author Central, Cristian Molinaro, , V.S. Subrahmanian, Leila Amgoud