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Algorithms for Logical Knowledge Bases


This project deals with the exploration of algorithms and methods in the context of logical knowledge bases (KBs). It is a joint collaboration with researchers from Kyoto University (Japan). The goal is to provide efficient algorithms for various computational problems on knowledge bases which are formulated in a propositional language. Different issues are researched.

Data and knowledge acquisition. Partially defined Boolean functions (PDBFs) have been proposed for representing information about positive and negative logical correlation of facts. One task is to construct from such data a KB which is consistent with these data, i.e., a Boolean function (usually restricted to some class) which interpolates on these data (called extension). This amounts to specific tasks in the machine learning area.

In our project, properties and algorithms for extensions from different classes of Boolean functions have been explored.

Tractable Inference. Traditionally, pieces of knowledge in KB are represented through logical formulas, which comprise facts and rules. A major drawback of this form of representation is that already in plain languages such as propositional logic, fundamental reasoning problems (e.g., deciding satisfiability or entailment of a formula) are computationally intractable. To cope with this, two main directions have been explored.

In our project, we explore algorithms and complexity issues for approaches in both of the two directions.

Selected Publications


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