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Abductive Logic

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Abductive reasoning is a form of backward reasoning from an abductive knowledge base that contains incomplete observations to the best prediction (maybe True). Abductive reasoning allows inferring precondition E as an explanation of P. As a result of this inference, abduction allows the precondition E to be abducted from the consequence P. In contrast to deductive reasoning, Non-Certainty and Non-Monotonicity (and therefore defeasibility) are properties of abductive reasoning.
Today, abductive reasoning is an important matter in logic, Philosophy of Sciences, Methodology, computer science, Artificial intelligence, medicine and etc. Also, Diagnostic expert systems frequently employ abduction. In knowledge graph field, ABox abduction algorithms are efficient and effective algorithms in representing the knowledge and inference of conjectural queries and finding the best explanations.

In the Borhan team we have “abductive logic” course to learn what it is and how can be used in semantic web and machine reasoning.

For more info and attending the course mail us.

Here is our “Abductive Logic” course outline:

+ What is abduction?

            + Types of reasoning

            + Abduction in diagnostics

+ Computational instruments

           + Resolution Algorithms

           + Tableau Algorithms

+ Description dynamic Logic

           + Abductive problems

           + Semantics

           + Syntax

           + Tableau-Resolution Algorithms

+ Implementation

          + In java and Pellet

          + In protégé ?

+ Challenges

          + Complexity (in time and space)

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