Adaptive Logics for Defeasible Reasoning: Applications in by Christian Straßer

Logic

By Christian Straßer

This ebook provides adaptive logics as an intuitive and strong framework for modeling defeasible reasoning. It examines quite a few contexts during which defeasible reasoning turns out to be useful and gives a compact creation into adaptive logics.

The writer first familiarizes readers with defeasible reasoning, the adaptive logics framework, mixtures of adaptive logics, and various invaluable meta-theoretic homes. He then bargains a scientific learn of adaptive logics in response to a number of purposes.

The booklet provides formal versions for defeasible reasoning stemming from diversified contexts, comparable to default reasoning, argumentation, and normative reasoning. It highlights numerous meta-theoretic benefits of adaptive logics over different logics or logical frameworks that version defeasible reasoning. during this method the publication substantiates the prestige of adaptive logics as a common formal framework for defeasible reasoning.

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Extra info for Adaptive Logics for Defeasible Reasoning: Applications in Argumentation, Normative Reasoning and Default Reasoning

Example text

This characterization is attractive from a model-theoretic perspective since it is formulated independent of the consequence relation of the LLL which was used in the original definition in order to characterize the set U (Γ ). It is formulated only in terms of properties of the LLL-models of Γ , just like the definition of the semantic selection for the minimal abnormality strategy. 2 The Proof Theory The proof theory for minimal abnormality differs from the one for reliability only with respect to the marking definition.

Let me explicate this again by our example. Note first that in CL◦ the following rules are not valid: If ◦ A, then A. If ◦ A and A ⊃ B, then B. A is the case. 2). A}”. A can be considered not to be the case (see Fig. 2). This is still an ambiguous phrase and has different readings according to the two strategies. Fig. 2 Conditional inference A A defeasible ¬( A ∧ ¬A ) assumption: 7 In order to reduce notational clutter I will often omit set brackets on the left hand side of . 4 The Adaptive Strategy 19 For the reliability strategy this is spelled out as follows: deriving A “on the condition Δ” means that A is derived on the condition that no member of Δ is unreliable.

X entered the scene of crime alone right before the shot was heard, then he must be the murderer: (a ∧ n) ⊃ c. Similarly, (b ∧ n) ⊃ c. What makes the situation more complicated is that our detective has definite proof that at least one of the witnesses has been bribed by one of Mr. X’s enemies in order to fake a witness statement. Hence, since one of the witnesses lies, we have ¬a ∨ ¬b. 1 The Reliability Strategy If she takes a cautious stance, she will not conclude that Mr. X is the murderer since after all, both of the witnesses may be bribed.

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