Managing temporal cycles in planning problems requiring concurrency

To correctly model certain real-world planning problems, it is essential to take into account time. This is the case for problems requiring the concurrent execution of actions (known as temporally expressive problems). In this paper, we define and study the notion of temporally cyclic problems, that is problems involving sets of cyclically dependent actions. We characterize those temporal planning languages, which can express temporally cyclic problems. We also present a polynomial-time algorithm, which transforms a temporally cyclic problem into an equivalent acyclic problem. Applying our transformation allows any temporal planner to solve temporally cyclic problems without explicitly managing cyclicity. We first present our results for temporal PDDL (Planning Domain Description Language) 2.1 and then extend them to a language that allows conditions over arbitrary intervals and effects at arbitrary instants

Processes and continuous change in a SAT-based planner

AbstractThe TM-LPSAT planner can construct plans in domains containing atomic actions and durative actions; events and processes; discrete, real-valued, and interval-valued fluents; reusable resources, both numeric and interval-valued; and continuous linear change to quantities. It works in three stages. In the first stage, a representation of the domain and problem in an extended version of PDDL+ is compiled into a system of Boolean combinations of propositional atoms and linear constraints over numeric variables. In the second stage, a SAT-based arithmetic constraint solver, such as LPSAT or MathSAT, is used to find a solution to the system of constraints. In the third stage, a correct plan is extracted from this solution. We discuss the structure of the planner and show how planning with time and metric quantities is compiled into a system of constraints. The proofs of soundness and completeness over a substantial subset of our extended version of PDDL+ are presented

A SAT-based planning framework for optimizing resource production

Domain-independent automated planning is concerned with computing a sequence of actions that can transform an initial state into a desired goal state. Resource production domains form an interesting class of such problems, in that they typically require reasoning about concurrent durative-actions with continuous effects while minimizing some cost function. Although formulating planning problems as instances of SAT has proven to be very successful within the realm of STRIPS planning problems, where states and time are discrete and actions are instantaneous, it is unclear whether the same success can be transferred to resource production. Some of the major drawbacks to these systems are that they do not support reasoning about metric quantities, continuous time, and cost functions. TM-LPSAT was one of the first successful systems to reason about both metric quantities and continuous time within a SAT framework. However, TM-LPSAT does not provide a way to reason about cost functions. In this thesis, we extend the framework in a way that allows it to be capable of minimizing the costs, in our case makespans, of the plans that it finds

Efficient Automated Planning with New Formulations

Problem solving usually strongly relies on how the problem is formulated. This fact also applies to automated planning, a key field in artificial intelligence research. Classical planning used to be dominated by STRIPS formulation, a simple model based on propositional logic. In the recently introduced SAS+ formulation, the multi-valued variables naturally depict certain invariants that are missed in STRIPS, make SAS+ have many favorable features. Because of its rich structural information SAS+ begins to attract lots of research interest. Existing works, however, are mostly limited to one single thing: to improve heuristic functions. This is in sharp contrast with the abundance of planning models and techniques in the field. On the other hand, although heuristic is a key part for search, its effectiveness is limited. Recent investigations have shown that even if we have almost perfect heuristics, the number of states to visit is still exponential. Therefore, there is a barrier between the nice features of SAS+ and its applications in planning algorithms. In this dissertation, we have recasted two major planning paradigms: state space search and planning as Satisfiability: SAT), with three major contributions. First, we have utilized SAS+ for a new hierarchical state space search model by taking advantage of the decomposable structure within SAS+. This algorithm can greatly reduce the time complexity for planning. Second, planning as Satisfiability is a major planning approach, but it is traditionally based on STRIPS. We have developed a new SAS+ based SAT encoding scheme: SASE) for planning. The state space modeled by SASE shows a decomposable structure with certain components independent to others, showing promising structure that STRIPS based encoding does not have. Third, the expressiveness of planning is important for real world scenarios, thus we have also extended the planning as SAT to temporally expressive planning and planning with action costs, two advanced features beyond classical planning. The resulting planner is competitive to state-of-the-art planners, in terms of both quality and performance. Overall, our work strongly suggests a shifting trend of planning from STRIPS to SAS+, and shows the power of formulating planning problems as Satisfiability. Given the important roles of both classical planning and temporal planning, our work will inspire new developments in other advanced planning problem domains

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