Planification SAT et Planification Temporellement Expressive. Les Systèmes TSP et TLP-GP.

This thesis deals with Artificial Intelligence planning. After introducing the domain and the main algorithms in the classical framework of planning, we present a state of the art of SAT planning. We analyse in detail this approach which allows us to benefit directly from improvements brought regularly to SAT solvers. We propose new encodings integrating a least-commitment strategy postponing as much as possible the scheduling of actions. We then introduce the TSP system which we have implemented to equitably compare the different encodings and we detail the results of numerous experimental tests which show the superiority of our encodings in comparison with the existing ones. We introduce then a state of the art of temporal planning by analysing algorithms and expressiveness of their representation languages. The great majority of these planners do not allow us to solve real problems for which the concurrency of actions is required. We then detail the two original approaches of our TLP-GP system which allow us to solve this type of problem. As with SAT planning, a large part of search work is delegated to a SMT solver. We then propose extensions of the PDDL planning language which allows us to a certain extent to take into account uncertainty, choice, or continuous transitions. We show finally, thanks to an experimental study, that our algorithms allow us to solve real problems requiring numerous concurrent actions.Cette thèse s'inscrit dans le cadre de la planification de tâches en intelligence artificielle. Après avoir introduit le domaine et les principaux algorithmes de planification dans le cadre classique, nous présentons un état de l'art de la planification SAT. Nous analysons en détail cette approche qui permet de bénéficier directement des améliorations apportées régulièrement aux solveurs SAT. Nous proposons de nouveaux codages qui intègrent une stratégie de moindre engagement en retardant le plus possible l'ordonnancement des actions. Nous présentons ensuite le système TSP que nous avons implémenté pour comparer équitablement les différents codages puis nous détaillons les résultats de nombreux tests expérimentaux qui démontrent la supériorité de nos codages par rapport aux codages existants. Nous présentons ensuite un état de l'art de la planification temporelle en analysant les algorithmes et l'expressivité de leurs langages de représentation. La très grande majorité de ces planificateurs ne permet pas de résoudre des problèmes réels pour lesquels la concurrence des actions est nécessaire. Nous détaillons alors les deux approches originales de notre système TLP-GP permettant de résoudre ce type de problèmes. Ces approches sont comparables à la planification SAT, une grande partie du travail de recherche étant déléguée à un solveur SMT. Nous proposons ensuite des extensions du langage de planification PDDL qui permettent une certaine prise en compte de l'incertitude, du choix, ou des transitions continues. Nous montrons enfin, grâce à une étude expérimentale, que nos algorithmes permettent de résoudre des problèmes réels nécessitant de nombreuses actions concurrentes

Task scheduling and merging in space and time

Every day, robots are being deployed in more challenging environments, where they are required to perform complex tasks. In order to achieve these tasks, robots rely on intelligent deliberation algorithms. In this thesis, we study two deliberation approaches – task scheduling and task planning. We extend these approaches in order to not only deal with temporal and spatial constraints imposed by the environment, but also exploit them to be more efficient than the state-of-the-art approaches. Our first main contribution is a scheduler that exploits a heuristic based on Allen’s interval algebra to prune the search space to be traversed by a mixed integer program. We empirically show that the proposed scheduler outperforms the state of the art by at least one order of magnitude. Furthermore, the scheduler has been deployed on several mobile robots in long-term autonomy scenarios. Our second main contribution is the POPMERX algorithm, which is based on merging of partially ordered temporal plans. POPMERX first reasons with the spatial and temporal structure of separately generated plans. Then, it merges these plans into a single final plan, while optimising the makespan of the merged plan. We empirically show that POPMERX produces better plans that the-state-ofthe- art planners on temporal domains with time windows