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

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 explores that there are three main algorithmic techniques
for solving constraint satisfaction problems:(1) backtracking search,(2) local search, and (3) …

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

Abstract We propose Hybrid Best-First Search (HBFS), a search strategy for optimization
problems that combines Best-First Search (BFS) and Depth-First Search (DFS). Like BFS …

Standard planners for sequential decision making (including Monte Carlo planning, tree
search, dynamic programming, etc.) are constrained by an implicit sequential planning …

In Computational Protein Design (CPD), assuming a rigid backbone and amino-acid rotamer
library, the problem of finding a sequence with an optimal conformation is NP-hard. In this …

Several recent approaches for processing graphical models (constraint and Bayesian
networks) simultaneously exploit graph decomposition and local consistency enforcing …

Inspired by the recently introduced framework of AND/OR search spaces for graphical
models, we propose to augment Multi-Valued Decision Diagrams (MDD) with AND nodes, in …

Graphical models (GMs) define a family of mathematical models aimed at the concise
description of multivariate functions using decomposability. We restrict ourselves to functions …