In this paper we report on our experience parallelizing probabilistic roadmap motion planning methods (prms). We show that signi cant, scalable speedups can be obtained with relatively little e ort on the part of the developer. Our experience is not limited to prms, however. In particular, we outline general techniques for parallelizing types of computations commonly performed in motion plannin...

In a graph G, the distance from an edge e to a set F ⊆ E(G) is the vertex distance from e to F in the line graph L(G). For a decomposition of E(G) into k sets, the distance vector of e is the k-tuple of distances from e to these sets. The decomposition dimension dec(G) of G is the smallest k such that G has a decomposition into k sets so that the distance vectors of the edges are distinct. For ...

Numerous problems in Graph Theory and Combinatorics can be formulated in terms of the existence of certain colorings of graphs or hypergraphs. Many of these problems can be solved or partially solved by applying probabilistic arguments. In this paper we discuss several examples that illustrate the methods used. This is mainly a survey paper, but it contains some new results as well.

The eld of Arti cial Intelligence (AI) is currently undergoing a transition. While in the eighties, rule-based and logical representations were the representation of choice in the majority of AI systems, in recent years various researchers have explored alternative representational frameworks, which emphasis on frameworks that enable systems to represent and handle uncertainty. Out of those, pr...

In this chapter the authors review probabilistic approaches to face recognition and present extended treatment of one particular approach. Here, the face image is decomposed into an additive sum of two parts: a deterministic component, which depends on an underlying representation of identity and a stochastic component which explains the fact that two face images from the same person are not id...

Probabilistic Methods for Multiscale Evolutionary Dynamics by Shishi Luo Department of Mathematics Duke University

By Fuzzy theory we mean Zadeh’s mathematical theory of generalized sets and their associated logics. At this state of the art, fuzzy theory has provided significant contributions to artificial intelligence via a broader umbrella of soft computing. Like other theories of mathematics, such as number theory and potential theory, fuzzy theory has its own agenda, concepts, and techniques. However, j...

Through this paper, some performance increasing methods for probabilistic neural network (PNN) are presented. These methods are tested with the glass benchmark database which has an irregular class distribution. Selection of a good training dataset is one of the most important issue. Therefore, a new data selection procedure was proposed. A data replication method is applied to the rare events ...

The analysis of decision making under uncertainty is closely related to the analysis of probabilistic inference. Indeed, much of the research into efficient methods for probabilistic inference in expert systems has been motivated by the fundamental nonnative arguments of decision theory. In this paper we show how the developments underlying those efficient methods can be applied immediately to ...

Model Checking is a fully automatic verification method that has undergone a vast development for almost 30 years now. In contrast to simulation and testing, model checking is a verification technique that explores all possible system states exhaustively and can therefore reveal errors that have not been discovered by testing or simulation. It thus is a prominent verification technique for safe...