Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. A network of this sort can be used

Abstract. Merging multi-sourced ontologies in a consistent manner is an important and challenging research topic. In this paper, we propose a novel approach for merging DL-Lite N bool ontologies by adapting the classical model-based belief merging approach, where the minimality of changes

In this paper we describe two approaches to the revision of probability functions. We assume that a probabilistic state of belief is captured by a counterfactual probability or Popper function, the revision of which determines a new Popper function. We describe methods whereby the original function

of multiple motions within a patch which arise from occlusion boundaries or transparency. W e present a new approach which allows us to deal with these issues within a common framework. Our approach is based on the use of a probabilistic mixture m o del to explicitly represent m ultiple motions within a patch

Abstract — When dealing with massive quantities of data, topk queries are a powerful technique for returning only the k most relevant tuples for inspection, based on a scoring function. The problem of efficiently answering such ranking queries has been studied and analyzed extensively within

Genetic Algorithms are a common probabilistic optimization method based on the model of natural evolution. One important operator in these algorithms is the selection scheme used to prefer better individuals. In this paper a new description model for selection schemes is introduced that operates

Dempster-Shafer (DS) theory is a powerful tool for probabilistic reasoning and decision-making based on a formal calculus for combining statistical and non-statistical evidence, as represented by a system of belief functions. DS theory has been widely used in computer science and engineering

that solving QSAT 3 ∈ Σ3Time(n · poly log n) requires time n 1+Ω(1) on probabilistic Turing machines using space n.9, with random access to input and work tapes, and two-way sequential access to the random-bit tape. This is the first lower bound of the form t = n 1+Ω(1) on a model with random access

Causal probabilistic networks have proved to be a useful knowledge representation tool for modelling domains where causal relations in a broad sense are a natural way of relating domain objects and where uncertainty is inherited in these relations. This paper outlines an implementation the HUGIN

The main result of this paper is a new algorithm for constructing an irreducible polynomial of specified degree n over a finite field F q . The algorithm is probabilistic, and is asymptotically faster than previously known algorithms for this problem. It uses an expected number of O~(n 2 + n log q