A General Framework for Reasoning On Inconsistency by Maria Vanina Martinez, Visit Amazon's Cristian Molinaro PDF

By Maria Vanina Martinez, Visit Amazon's Cristian Molinaro Page, search results, Learn about Author Central, Cristian Molinaro, , V.S. Subrahmanian, Leila Amgoud

ISBN-10: 1461467497

ISBN-13: 9781461467496

ISBN-10: 1461467500

ISBN-13: 9781461467502

This SpringerBrief proposes a basic framework for reasoning approximately inconsistency in a wide selection of logics, together with inconsistency answer equipment that experience now not but been studied. The proposed framework permits clients to specify personal tastes on find out how to unravel inconsistency whilst there are a number of how one can accomplish that. This empowers clients to solve inconsistency in information leveraging either their precise wisdom of the information in addition to their software wishes. The short exhibits that the framework is well-suited to deal with inconsistency in different logics, and gives algorithms to compute most well liked recommendations. eventually, the short exhibits that the framework not just captures a number of present works, but in addition helps reasoning approximately inconsistency in different logics for which no such equipment exist today.

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T. 1 ≤ j < i, |X j | = |Y j |. Let us consider the best-out preference and let amax(K ) = max{i | K1 ∪ . . ∪ Ki is consistent}. If amax(K ) = k, then the best-out preferred consistent subbases of K are exactly the consistent subbase of K which contain (K1 ∪ . . ∪ Kk ); we denote them by Pbo (K ). This approach can be easily captured by our framework by adopting W⊆ as weakening mechanism and defining the preference relation as follows. 7. Consider a knowledge base K and let W⊆ be the adopted weakening mechanism.

Given a literal of the form x (resp. ¬x), with x ∈ X, we denote with p( ) the propositional variable xT (resp. xF ). Let K1 = { (u ← p( 1 ) ∧ p( 2 ) ∧ p( 3 )) | 1∧ 2∧ 3 is a con junction o f φ } and K2 = { (u ← xT ∧ xF ) | x ∈ X} Given a variable x ∈ X, let Kx = { xT , xF , (← xT ∧ xF )} Finally, K ∗ = K1 ∪ K2 ∪ Kx x∈X The derived instance of our problem is (K ∗ , u). 2. 4 Fuzzy Logic In this section we consider fuzzy logic. Formulas are of the form φ : v, where φ is a propositional formula built from a set Σ of propositional symbols and the logical connectives ¬, ∧, ∨, and v ∈ [0, 1] (we call v the degree of truth).

However, {Bp, ¬Bp} is inconsistent. 6. Consider the following inconsistent knowledge base K that represents the knowledge of an agent regarding a city’s subway system: ψ1 : goingNorthTrain1 ψ2 : B goingNorthTrain1 ψ3 : goingNorthTrain1 → canGetUpTownFromStationA ψ4 : B(goingNorthTrain1 → canGetUpTownFromStationA) ψ5 : ¬(canGetUpTownFromStationA) Using a train schedule associated with train station A, we might be able to express formulas ψ1 and ψ3 . ψ1 states that Train 1 goes north, whereas ψ3 states that if Train 1 goes north, then the agent can get uptown from station A.

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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


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