Progress in AI Planning Research and Applications

Planning has made significant progress since its inception in the 1970s, in terms both of the efficiency and sophistication of its algorithms and representations and its potential for application to real problems. In this paper we sketch the foundations of planning as a sub-field of Artificial Intelligence and the history of its development over the past three decades. Then some of the recent achievements within the field are discussed and provided some experimental data demonstrating the progress that has been made in the application of general planners to realistic and complex problems. The paper concludes by identifying some of the open issues that remain as important challenges for future research in planning

Constructing Conditional Plans by a Theorem-Prover

The research on conditional planning rejects the assumptions that there is no uncertainty or incompleteness of knowledge with respect to the state and changes of the system the plans operate on. Without these assumptions the sequences of operations that achieve the goals depend on the initial state and the outcomes of nondeterministic changes in the system. This setting raises the questions of how to represent the plans and how to perform plan search. The answers are quite different from those in the simpler classical framework. In this paper, we approach conditional planning from a new viewpoint that is motivated by the use of satisfiability algorithms in classical planning. Translating conditional planning to formulae in the propositional logic is not feasible because of inherent computational limitations. Instead, we translate conditional planning to quantified Boolean formulae. We discuss three formalizations of conditional planning as quantified Boolean formulae, and present experimental results obtained with a theorem-prover

The GRT Planning System: Backward Heuristic Construction in Forward State-Space Planning

This paper presents GRT, a domain-independent heuristic planning system for STRIPS worlds. GRT solves problems in two phases. In the pre-processing phase, it estimates the distance between each fact and the goals of the problem, in a backward direction. Then, in the search phase, these estimates are used in order to further estimate the distance between each intermediate state and the goals, guiding so the search process in a forward direction and on a best-first basis. The paper presents the benefits from the adoption of opposite directions between the preprocessing and the search phases, discusses some difficulties that arise in the pre-processing phase and introduces techniques to cope with them. Moreover, it presents several methods of improving the efficiency of the heuristic, by enriching the representation and by reducing the size of the problem. Finally, a method of overcoming local optimal states, based on domain axioms, is proposed. According to it, difficult problems are decomposed into easier sub-problems that have to be solved sequentially. The performance results from various domains, including those of the recent planning competitions, show that GRT is among the fastest planners

The Automatic Inference of State Invariants in TIM

As planning is applied to larger and richer domains the effort involved in constructing domain descriptions increases and becomes a significant burden on the human application designer. If general planners are to be applied successfully to large and complex domains it is necessary to provide the domain designer with some assistance in building correctly encoded domains. One way of doing this is to provide domain-independent techniques for extracting, from a domain description, knowledge that is implicit in that description and that can assist domain designers in debugging domain descriptions. This knowledge can also be exploited to improve the performance of planners: several researchers have explored the potential of state invariants in speeding up the performance of domain-independent planners. In this paper we describe a process by which state invariants can be extracted from the automatically inferred type structure of a domain. These techniques are being developed for exploitation by STAN, a Graphplan based planner that employs state analysis techniques to enhance its performance

Un modèle de composition automatique et distribuée de services web par planification

National audienceWeb services advent as an inevitable technology of the Web and its dissimination on a large scale, poses the problem of their automatic composition. Indeed, one of the most im- portant obstacle to the development of web services oriented architectures relies on the manual generation of composite services by human experts. In order to overtake this approach, we propose in this article a novel architecture for web services composition based on planning techniques. Its originality consists in its completely distributed planning model where agents reason together on their own services to achieve a shared goal defined by users and where the global shared plan built stand for a possible composition of their services.L'avènement des services web comme une technologie incontournable du web et sa dissémination à grande échelle pose dorénavant la problématique de leur composition automa- tique. En effet, l'un des verrous les plus importants au développement des architectures orien- tées services réside dans l'élaboration manuelle par un expert de services composites. Afin de répondre à cette problématique, nous proposons dans cet article une architecture originale de composition automatique de services web par des techniques de planification. Son originalité repose sur la conception d'un modèle de planification entièrement distribué dans lequel les agents raisonnent conjointement sur leurs services respectifs pour atteindre un but commun prédéfini par l'utilisateur, créant ainsi un plan global représentant une composition possible de leurs services

Un modèle de composition automatique et distribuée de services web par planification

National audienceWeb services advent as an inevitable technology of the Web and its dissimination on a large scale, poses the problem of their automatic composition. Indeed, one of the most im- portant obstacle to the development of web services oriented architectures relies on the manual generation of composite services by human experts. In order to overtake this approach, we propose in this article a novel architecture for web services composition based on planning techniques. Its originality consists in its completely distributed planning model where agents reason together on their own services to achieve a shared goal defined by users and where the global shared plan built stand for a possible composition of their services.L'avènement des services web comme une technologie incontournable du web et sa dissémination à grande échelle pose dorénavant la problématique de leur composition automa- tique. En effet, l'un des verrous les plus importants au développement des architectures orien- tées services réside dans l'élaboration manuelle par un expert de services composites. Afin de répondre à cette problématique, nous proposons dans cet article une architecture originale de composition automatique de services web par des techniques de planification. Son originalité repose sur la conception d'un modèle de planification entièrement distribué dans lequel les agents raisonnent conjointement sur leurs services respectifs pour atteindre un but commun prédéfini par l'utilisateur, créant ainsi un plan global représentant une composition possible de leurs services

Compiling Causal Theories to Successor State Axioms and STRIPS-Like Systems

We describe a system for specifying the effects of actions. Unlike those commonly used in AI planning, our system uses an action description language that allows one to specify the effects of actions using domain rules, which are state constraints that can entail new action effects from old ones. Declaratively, an action domain in our language corresponds to a nonmonotonic causal theory in the situation calculus. Procedurally, such an action domain is compiled into a set of logical theories, one for each action in the domain, from which fully instantiated successor state-like axioms and STRIPS-like systems are then generated. We expect the system to be a useful tool for knowledge engineers writing action specifications for classical AI planning systems, GOLOG systems, and other systems where formal specifications of actions are needed