The distributed constraint satisfaction problem is a general formalization used to represent problems in distributed multi-agent systems. A large body of problems in artificial intelligence and computer science can be easily formulated as distributed constraint satisfaction problems. In this thesis we study agent ordering, effects of no-goods, search efficiency and threshold repairing in distributed constraint satisfaction problems and its variants. A summary of contributions is as follows: 1. We present a new algorithm, Dynamic Agent Ordering. A distinctive feature of this algorithm is that it uses the degree of unsatisfiability as a guiding parameter to dynamically determine agent ordering during the search. We show through an empirical study that our algorithm performs better than the existing approaches. In our approach, the independence of agents is guaranteed and agents without neighbouring relationships can run concurrently and asynchronously. (Part of this work was published in the Australian Al Conference (80)). 2. We extend the Dynamic Agent Ordering algorithm by incorporating a novel technique called nogood repairing. This results in a dramatic reduction in the nogoods being stored, and communication costs. In an empirical study, we11 show that this approach outperforms an equivalent static ordering algorithm and a current state-of-the-art technique in terms of execution time, memory usage and communication cost. (Part of this work was published at FLAIRS Conference (81)). Further, we introduce a new algorithm, Over-constrained Dynamic Agent Ordering, that breaks new ground in handling multiple variables per agent in distributed over-constrained satisfaction problems. The algorithm also uses the degree of unsatisfiability as a measure for relaxing constraints, and hence as a way to guide the search toward the best optimal solution(s). By applying our Threshold Repair method, we can solve a distributed constraint satisfaction problem without knowing whether the problem is under- or over-constrained. In an experimental study, we show that the new algorithm compares favourably to an implementation of asynchronous weak commitment search adapted to handle over-constrained problems. (Part of this work was published at the Canadian AI conference (79)).

En août 2005, British Airways mit quatre jours à rétablir ses vols après une grève d'une journée d'un de ses sous-traitants. En interconnectant et intégrant leurs systèmes d'aide à la décision, les entreprises deviennent de plus en plus soumises aux changements. Parallèlement, avec le développement de technologies comme les puces RFID et la localisation par GPS les entreprises sont capables de suivre en temps réel le déroulement des opérations sur le terrain. Dans cette thèse nous nous intéressons au cas des problèmes de satisfaction de contraintes dans un cadre dynamique. En effet, depuis de nombreuses années la programmation par contraintes a fait la preuve de son efficacité pour résoudre des problèmes d'optimisation (tournées de véhicules, ordonnancements, etc) dans le cadre statique. Cependant le contexte dynamique soulève encore de nombreuses difficultés. Nous proposons donc un ensemble d'outils permettant la gestion et la prise en compte des événements, survenant de manière inattendue, dans le cadre de la programmation par contraintes. Chaque outil repose sur une approche particulière des problèmes dynamiques (tuples interdits, recherche locale, explications) et offre ainsi un éclairage différent et complémentaire.