A large variety of problems in Artificial Intelligence and other areas of computer science can be viewed as a special case of the constraint satisfaction problem. Some examples are machine vision, belief maintenance, scheduling, temporal reasoning, graph problems, floor plan design, planning genetic experiments, and the satisfiability problem. A number of different approaches have been developed for solving these problems. Some of them use constraint propagation to simplify the original problem. Others use backtracking to directly search for possible solutions. Some are a combination of these two techniques. This paper presents a brief overview of many of these approaches in a tutorial fashion.

Abstract-Relaxation labeling processes have been widely used in many different domains including image processing, pattern recognition, and artificial intelligence. They are iterative procedures that aim at reducing local ambiguities and achieving global consistency through a parallel exploitation of contextual information, which is quantitatively expressed in terms of a set of “compatibility coefficients. ” The problem of determining compatibility coefficients has received a considerable attention in the past and many heuristic, statistical-based methods have been suggested. In this paper, we propose a rather different viewpoint to solve this problem: we derive them attempting to optimize the performance of the relaxation algorithm over a sample of training data; no statistical interpretation is given: compatibility coefficients are simply interpreted as real numbers, for which performance is optimal. Experimental results over a novel application of relaxation are given, which prove the effectiveness of the proposed approach. Index Terms- Compatibility coefficients, constraint satisfaction, gradient projection, learning, neural networks, nonlinear

This paper addresses the problem of reliably setting genetic algorithm parameters for consistent labelling problems. Genetic algorithm parameters are notoriously difficult to determine. This paper proposes a robust empirical framework, based on the analysis of factorial experiments. The use of a graeco-latin square permits an initial study of a wide range of parameter settings. This is followed by fully crossed factorial experiments with narrower ranges, which allow detailed analysis by logistic regression. The empirical models thus derived can be used first to determine optimal algorithm parameters, and second to shed light on interactions between the parameters and their relative importance. The initial models do not extrapolate well. However, an advantage of this approach is that the modelling process is under the control of the experimenter, and is hence very flexible. Refined models are produced, which are shown to be robust under extrapolation to up to triple the problem size.

Dynamic constraint satisfaction is a useful tool for representing and solving sequential decision problems with complete knowledge in dynamic world and particularly constrained resource allocation problems. However, when resources are unreliable, this framework becomes limited due to the stochastic outcomes of the assignments chosen. On the contrary, Markov Decision Processes (MDPs) handle stochastic outcomes of unreliable actions, but their complexity explodes when using state-defined constraints. We thus propose an extension of the MDP framework so as to represent constrained and stochastic actions in sequential decision making. The basis of this extension consists in modeling the evolution of a dynamic constraint network by a MDP. We first study the complexity of the problem of finding an optimal policy for this model and then we propose an algorithm for solving it. Comparison to standard MDP shows that this framework noticeably improves policy computation.

Cooperating local parallel ,processes can be used as aids in assigning numerical or symbolic labels to image or scene parts. Various approaches to using such processes in low-level vision are reviewed, and their advantages are discussed. Methods of designing and controlling such proces- ses are also considered.