The Complexity of Planning Revisited - A Parameterized Analysis

The early classifications of the computational complexity of planning under various restrictions in STRIPS (Bylander) and SAS+ (Baeckstroem and Nebel) have influenced following research in planning in many ways. We go back and reanalyse their subclasses, but this time using the more modern tool of parameterized complexity analysis. This provides new results that together with the old results give a more detailed picture of the complexity landscape. We demonstrate separation results not possible with standard complexity theory, which contributes to explaining why certain cases of planning have seemed simpler in practice than theory has predicted. In particular, we show that certain restrictions of practical interest are tractable in the parameterized sense of the term, and that a simple heuristic is sufficient to make a well-known partial-order planner exploit this fact.Comment: (author's self-archived copy

Parameterized Complexity Results for Plan Reuse

Planning is a notoriously difficult computational problem of high worst-case complexity. Researchers have been investing significant efforts to develop heuristics or restrictions to make planning practically feasible. Case-based planning is a heuristic approach where one tries to reuse previous experience when solving similar problems in order to avoid some of the planning effort. Plan reuse may offer an interesting alternative to plan generation in some settings. We provide theoretical results that identify situations in which plan reuse is provably tractable. We perform our analysis in the framework of parameterized complexity, which supports a rigorous worst-case complexity analysis that takes structural properties of the input into account in terms of parameters. A central notion of parameterized complexity is fixed-parameter tractability which extends the classical notion of polynomial-time tractability by utilizing the effect of structural properties of the problem input. We draw a detailed map of the parameterized complexity landscape of several variants of problems that arise in the context of case-based planning. In particular, we consider the problem of reusing an existing plan, imposing various restrictions in terms of parameters, such as the number of steps that can be added to the existing plan to turn it into a solution of the planning instance at hand.Comment: Proceedings of AAAI 2013, pp. 224-231, AAAI Press, 201

A CASE FOR DOMAIN-INDEPENDENT DETERMINISTIC MULTIAGENT

The notion of planning using multiple agents has been around since the very beginning of planning itself. It has been approached from various viewpoints especially in the multiagent systems community. Recently, domain-independent multiagent planning has gained more attention also in the automated planning community. In this paper, we shortly present the current state of the art, question some aspects of the research field and discuss the rising challenges

Décomposition des problèmes de planification de tâches basée sur les landmarks

The algorithms allowing on-the-fly computation of efficient strategies solving a heterogeneous set of problems has always been one of the greatest challenges faced by research in Artificial Intelligence. To this end, classical planning provides to a system reasoning capacities, in order to help it to interact with its environment autonomously. Given a description of the world current state, the actions the system is able to perform, and the goal it is supposed to reach, a planner can compute an action sequence yielding a state satisfying the predefined goal. The planning problem is usually intractable (PSPACE-hard), however some properties of the problems can be automatically extracted allowing the design of efficient solvers.Firstly, we have developed the Landmark-based Meta Best-First Search (LMBFS) algorithm. Unlike state-of-the-art planners, usually based on state-space heuristic search, LMBFS reenacts landmark-based planning problem decomposition. A landmark is a fluent appearing in each and every solution plan. The LMBFS algorithm splits the global problem in a set of subproblems and tries to find a global solution using the solutions found for these subproblems. Secondly, we have adapted classical planning techniques to enhance the performance of our base algorithm, making LMBFS a competitive planner. Finally, we have tested and compared these methods.Les algorithmes permettant la création de stratégies efficaces pour la résolution d’ensemble de problèmes hétéroclites ont toujours été un des piliers de la recherche en Intelligence Artificielle. Dans cette optique, la planification de tâches a pour objectif de fournir à un système la capacité de raisonner pour interagir avec son environnement de façon autonome afin d’atteindre les buts qui lui ont été assignés. À partir d’une description de l’état initial du monde, des actions que le système peut exécuter, et des buts qu’il doit atteindre, un planificateur calcule une séquence d’actions dont l’exécution permet de faire passer l’état du monde dans lequel évolue le système vers un état qui satisfait les buts qu’on lui a fixés. Le problème de planification est en général difficile à résoudre (PSPACE-difficile), cependant certaines propriétés des problèmes peuvent être automatiquement extraites permettant ainsi une résolution efficace.Dans un premier temps, nous avons développé l’algorithme LMBFS (Landmarkbased Meta Best-First Search). À contre-courant des planificateurs state-of-the-art, basés sur la recherche heuristique dans l’espace d’états, LMBFS est un algorithme qui réactualise la technique de décomposition des problèmes de planification basés sur les landmarks. Un landmark est un fluent qui doit être vrai à un certain moment durant l’exécution de n’importe quel plan solution. L’algorithme LMBFS découpe le problème principal en un ensemble de sous-problèmes et essaie de trouver une solution globale grâce aux solutions trouvées pour ces sous-problèmes. Dans un second temps, nous avons adapté un ensemble de techniques pour améliorer les performances de l’algorithme. Enfin, nous avons testé et comparé chacune de ces méthodes permettant ainsi la création d’un planificateur efficace