Reasoning about an entity’s preferences (be it a user of an application, an individual targeted for marketing, or a group of people whose choices are of interest) has a long history in different areas of study. In this paper, we adopt the point of view that grows out of the intersection of databases and knowledge representation, where preferences are usually represented as strict partial orders...

A modal logic is any logic for handling modalities: concepts like possibility, necessity, and knowledge. Artificial intelligence uses modal logics most heavily to represent and reason about knowledge of agents about others’ knowledge. This type of reasoning occurs in dialog, collaboration, and competition. In many applications it is also important to be able to reason about the probability of b...

Probabilistic logic programming under the distribution semantics has been very useful in machine learning. However, inference is expensive so machine learning algorithms may turn out to be slow. In this paper we consider a restriction of the language called hierarchical PLP in which clauses and predicates are hierarchically organized. In this case the language becomes truth-functional and infer...

We present ProbLog2, the state of the art implementation of the probabilistic programming language ProbLog. The ProbLog language allows the user to intuitively build programs that do not only encode complex interactions between a large sets of heterogenous components but also the inherent uncertainties that are present in real-life situations. The system provides efficient algorithms for queryi...

Probabilistic logic models are used ever more often to deal with the uncertain relations typical of the real world. However, these models usually require expensive inference procedures. Very recently the problem of identifying tractable languages has come to the fore. In this paper we consider the models used by the learning from interpretations ILP setting, namely sets of integrity constraints...

In this paper, we introduce and define Probabilistic Boolean Logic, whose logical operators are “correct” with a probability 0 < p ≤ 1. Analogous to conventional Boolean logic, we define well-formed probabilistic Boolean formulae (pbf). Every pbf is associated with two attributes, the underlying Boolean function it computes, and a for a specific input, the probability that this boolean function...

This paper addresses two central problems for probabilistic processing models: parameter estimation from incomplete data and efficient retrieval of most probable analyses. These questions have been answered satisfactorily only for probabilistic regular and context-free models. We address these problems for a more expressive probabilistic constraint logic programming model. We present a log-line...

Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic logic programs. Several such tasks such as computing the marginals given evidence and learning from (partial) interpretations have not really been ...

Of all scientiic investigations into reasoning with uncertainty and chance, probability theory is perhaps the best understood paradigm. Nevertheless, all studies conducted thus far into the semantics of quantitative logic programming(cf.) have restricted themselves to non-probabilistic semantical characterizations. In this paper, we take a few steps towards rectifying this situation. We deene a...

After briefly mentioning the historical background of PLL/SRL, we examine PRISM, a logic-based modeling language, as an instance of PLL/SRL research. We first look at the distribution semantics, PRISM’s semantics, which defines a probability measure on a set of possible Herbrand models. We then mention characteristic features of PRISM as a tool for probabilistic modeling.