The paper provides a unifying perspective of tree-decomposition algorithms appearing in various automated reasoning areas such as jointree clustering for constraint-satisfaction and the clique-tree algorithm for probabilistic reasoning. By following the framework introduced by Shenoy [16], we then introduce a new algorithm, called buckettree elimination (BTE), that extends Bucket Elimination (B...

In this paper, the problem of finding the set of all real solutions to a system of n nonlinear general form algebraic equations contained in a given n-dimensional box (the global analysis problem) is considered. A new iterative interval method for solving the global analysis problem is suggested. It is based on the following techniques: (i) transformation of the original system into an augmente...

This paper shows an approach to model trajectory determination, using concurrent constraint programming, for autonomous multi-agent systems in charge of automatically managing the mission of an uninhabited vehicle. In the approach suggested by GP01] and BGP99], the task of the constraint solver is to automatically generate a plan for autonomous vehicles according to the changes of the environme...

In the last decades, the Satisfiability and Constraint Satisfaction Problem frameworks were extended to integrate aspects such as uncertainties, partial observabilities, or uncontrollabilities. The resulting formalisms, including Quantified Boolean Formulas (QBF), Quantified CSP (QCSP), Stochastic SAT (SSAT), or Stochastic CSP (SCSP), still rely on networks of local functions defining specific ...

Compiling graphical models has recently been under intense investigation, especially for probabilistic modeling and processing. We present here a novel data structure for compiling weighted graphical models (in particular, probabilistic models), called AND/OR Multi-Valued Decision Diagram (AOMDD). This is a generalization of our previous work on constraint networks, to weighted models. The AOMD...

A long-standing open research problem is how to use information from different experiments, including background knowledge, to infer causal relations. Recent developments have shown ways to use multiple data sets, provided they originate from identical experiments. We present the MCI-algorithm as the first method that can infer provably valid causal relations in the large sample limit from diff...

We consider the scheme of indeenite constraint databases proposed by Koubarakis. This scheme can be used to represent indefinite information arising in temporal, spatial and truly spatiotemporal applications. The main technical problem that we address in this paper is the discovery of tractable classes of databases and queries in this scheme. We start with the assumption that we have a class of...

In this study we discuss methods for solving random resistor networks and similar problems. We discuss the node elimination method and we demonstrate its equivalence to the Gaussian elimination scheme, finding a good elimination order, which makes the method highly efficient. The transfer matrix method is shown to be a special case of the node elimination method with an ordering that is far fro...