Thesis summaries : Stochastic reasoning with action probabilistic logic programs

In this thesis, we introduce action probabilistic (ap-) logic programs, a formalism for reasoning about the probability of events whose inter-dependencies are not known. Our approach is based on probabilistic logic programming, which is especially useful for this purpose since it allows the representation of imprecise probabilities. All work in this area up to now has focused on the problem of entailment (verifying if a query formula follows from the available knowledge). This thesis identifies and addresses several other complementary problems that need to be solved when deriving the probabilities of events: (i) computing most probable worlds, (ii) answering abduction queries; and (iii) reasoning about promises.We address different variants of these problems, propose exact and heuristic algorithms to scalably solve them, present empirical evaluations of their performance, and discuss their applications in real-world settings. © 2014 - IOS Press and the authors. All rights reserved.