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By Thomas Hubauer

Thomas Hubauer addresses the problem of offering moderate interpretations of incomplete observational facts within the context of imperfect area versions – a state of affairs usually encountered within the context of business diagnostics. To take on this challenge, the writer proposes a singular technique known as secure Abduction, which can derive pragmatic interpretations in occasions the place current equipment both fail or supply overly complicated recommendations. to bolster the hyperlink to functions in commercial diagnostics, he develops a strategy to constitution prognosis difficulties in accordance with ISO 13379 and exhibit it utilizing a number of description good judgment wisdom bases.

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Extra resources for Relaxed Abduction: Robust Information Interpretation for Industrial Applications

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1993), and point out the close relation to the principles of simplicity and consilience. Following Thagard (1978), consilience measures how much a description explains, making the explanatory power of competing explanations measurable. In this context, simplicity can then be seen as a restricting factor to consilience: As almost any observation can be explained by making a hypothesis arbitrarily complex, simplicity provides a measure of how efficiently this expressive power is realized. 1 Bi-Criteriality of Information Interpretation This antithetic character of simplicity and consilience is conceptually quite similar to the relationship between precision and recall in information retrieval, where alternative answer sets are typically weighed against each other based on a linear and complete order derived from precision and recall.

It is not hard to show (and in accordance with well-known results on goal-directed reasoning in general) that there exist cases that realize this worst case scenario. Solving a relaxed abduction problem is therefore a computationally non-trivial task. Nevertheless, the following example shows that already the natural, subsumption-based choice of (pre)orders leads to a significant reduction of the solution set size (and, therefore, computation time). )) As before, we consider the domain ontology T given by MCU Communications Transportation PROFINET Gripper Conveyor belongsTo ◦ subsystemOf ∃ partOf .

To address this potential drawback, we next take a look at a class of relaxed abduction problems that rely on semantic entailment. Entailment-based Orders Preference relations based on semantic entailment are a natural choice in the context of a logic-based reasoning framework such as relaxed abduction. As entailment is naturally an anti-monotonic preorder for set inclusion (S ⊆ S implies S =| S ), it is tempting to define simple entailment-based relaxed abduction by seRAP = (T , A, O, =|, |=). 2 (Subset- and simple entailment-based RAP are incomparable) Let T = ∅, A = {A(a), A A(a)}, O = {A(a)}.

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