We present an approach to knowledge compilation that transforms a function-free first-order Horn knowledge base to propositional logic. This form of compilation is important since the most efficient reasoning methods are defined for propositional logic, while knowledge is most conveniently expressed within a first-order language. To obtain compact propositional representations, we employ techniques from (ir)relevance reasoning as well as theory transformation via unfold/fold transformations. Application areas include diagnosis, planning, and vision. Preliminary experiments with a hypothetical reasoner indicate that our method may yield significant speed-ups.
|Number of pages||23|
|Journal||International Journal of Pattern Recognition and Artificial Intelligence|
|Publication status||Published - 2000 Feb|
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Control and Systems Engineering