Constraint programming is a powerful paradigm for solving combinatorial search problems
that draws on a wide range of techniques from artificial intelligence, computer science …

A problem that is inherent to the development and efficient use of solvers is that of tuning
parameters. The CP community has a long history of addressing this task automatically. We …

Distributed constraint optimization (DCOP) problems are a popular way of formulating and
solving agent-coordination problems. A DCOP problem is a problem where several agents …

The paper introduces an AND/OR search space perspective for graphical models that
include probabilistic networks (directed or undirected) and constraint networks. In contrast to …

Publisher Summary This chapter examines that several real-life combinatorial problems can
be naturally modelled and often efficiently solved using constraint techniques. It is …

Graphical models (eg, Bayesian and constraint networks, influence diagrams, and Markov
decision processes) have become a central paradigm for knowledge representation and …

Combinatorial Optimization is an important area of computer science that has many
theoretical and practical applications. In this thesis, we present important contributions to …

Abstract Machine learning systems are increasingly being used to make impactful decisions
such as loan applications and criminal justice risk assessments, and as such, ensuring …

This paper presents an algorithm for recovering the globally optimal 2D human figure
detection using a loopy graph model. This is computationally challenging because the time …

Abstract Inference in Bayes Nets (BAYES) is an important problem with numerous
applications in probabilistic reasoning. Counting the number of satisfying assignments of a …