Plog: Its algorithms and applications

This dissertation is a contribution towards combining logical and probabilistic reasoning. The work is based on the language P-log which combines a recently developed non-monotonic logic language, Answer Set Prolog, with the philosophy of causal Bayesian networks. The goal of this dissertation was to design and implement an inference engine for P-log and to develop a methodology of its use. As the result of this research work, we had built two P-log inference engines. Through the experiments on various examples, we have shown the advantages of using plog2.0, a system based on partial grounding algorithm and a concept of partial possible worlds. We introduced a new action description language NB which allows specifying non-deterministic actions as well as probabilities associated with these actions. We developed an encoding which maps systems written in NB to P-log programs. We presented systematic methods of representing probabilistic diagnosis and planning problems and algorithms of finding the solutions with P-log systems. Finally, we investigated the performance on these two applications and compared with other similar systems.