Abstract We propose a new model for forming and revising beliefs about unknown probabilities. To go beyond what is known with certainty represent the agent’s probability, we consider plausibility map, associating to each possible distribution ranking. Beliefs are defined as in Belief Revision Theory, terms of truth most plausible worlds (or more generally, all that enough). two forms conditioni...

We present an approach for recognizing highlevel geo-temporal phenomena – referred as events/occurrences– from in-depth discovery of information, using geo-tagged photos, formal event models, and various context cues like weather, space, time, and people. Due to the relative availability of information, our approach automatically obtains a probabilistic measure of occurrence likelihood for the ...

We present a concurrent separation logic with support for probabilistic reasoning. As part of our logic, we extend the idea of coupling, which underlies recent work on probabilistic relational logics, to the setting of programs with both probabilistic and non-deterministic choice. To demonstrate our logic, we verify a variant of a recent randomized concurrent counter algorithm. All of our resul...

A significant part of current research on ILP deals with probabilistic logical models. Over the last decade many logics or languages for representing such models have been introduced. There is currently a great need for insight into the relationships between all these languages. One class of languages are those that extend probabilistic models with elements of logic, such as in the language of ...

A new probabilistic-logic formalism, called CHRiSM, is introduced. CHRiSM is based on a combination of CHR and PRISM. It can be used for high-level rapid prototyping of complex statistical models by means of chance rules. The underlying PRISM system can then be used for several probabilistic inference tasks, including parameter learning. We describe a source-to-source transformation from CHRiSM...

The paper explores properties of Łukasiewicz μ-calculus, a version of the quantitative/probabilistic modal μ-calculus containing both weak and strong conjunctions and disjunctions from Łukasiewicz (fuzzy) logic. We show that this logic encodes the well-known probabilistic temporal logic PCTL. And we give a model-checking algorithm for computing the rational denotational value of a formula at an...

A popular family of probabilistic logic programming languages combines logic programs with independent probabilistic facts. We study the complexity of marginal inference, most probable explanations, and maximum a posteriori calculations for propositional/relational probabilistic logic programs that are acyclic/definite/stratified/normal/ disjunctive. We show that complexity classes Σk and PP Σk...