Juice is an open-source Julia package providing tools for logic and probabilistic reasoning learning based on circuits (LCs) (PCs). It provides a range of efficient algorithms inference queries, such as computing marginal probabilities (MAR), well many more advanced queries. Certain structural circuit properties are needed to achieve this tractability, which helps validate. Additionally, it sup...

The handling of uncertain information is of crucial importance for the success of expert systems. This paper gives an overview on logic-based approaches to probabilistic reasoning and goes into more details about recent developments for relational, respectively first-order, probabilistic methods like Markov logic networks, and Bayesian logic programs. In particular, we will feature the maximum ...

Article history: Received 17 April 2008 Available online 6 September 2008

Probabilistic logic models are used ever more often to deal with the uncertain relations that are typical of the real world. However, these models usually require expensive inference and learning procedures. Very recently the problem of identifying tractable languages has come to the fore. In this paper we consider the models used by the Inductive Constraint Logic (ICL) system, namely sets of i...

Towards sophisticated representation and reasoning techniques that allow for probabilistic uncertainty in the Rules, Logic, and Proof layers of the Semantic Web, we present probabilistic description logic programs (or pdl-programs), which are a combination of description logic programs (or dl-programs) under the answer set semantics and the well-founded semantics with Poole’s independent choice...

Before beginning the research that led to "Probabilistic logic" [11 ], I had participated with Richard Duda, Peter Hart, and Georgia Sutherland on the PROSPECTOR project [3]. There, we used Bayes' rule (with some assumptions about conditional independence) to deduce the probabilities of hypotheses about ore deposits given (sometimes uncertain) geologic evidence collected in the field [4]. At th...

Tractable subsets of first-order logic are a central topic in AI research. Several of these formalisms have been used as the basis for first-order probabilistic languages. However, these are intractable, losing the original motivation. Here we propose the first non-trivially tractable first-order probabilistic language. It is a subset of Markov logic, and uses probabilistic class and part hiera...

We study probabilistic logic under the viewpoint of the coherence principle of de Finetti. In detail, we explore how probabilistic reasoning under coherence is related to model-theoretic probabilistic reasoning and to default reasoning in System . In particular, we show that the notions of g-coherence and of g-coherent entailment can be expressed by combining notions in model-theoretic probabil...

BACKGROUND Clinical knowledge about progress of diseases is characterised by temporal information as well as uncertainty. However, precise timing information is often unavailable in medicine. In previous research this problem has been tackled using Allen's qualitative algebra of time, which, despite successful medical application, does not deal with the associated uncertainty. OBJECTIVES It i...