X and more Parallelism. Integrating LTL-Next into SAT-based Planning with Trajectory Constraints while Allowing for even more Parallelism

Linear temporal logic (LTL) provides expressive means to specify temporally extended goals as well as preferences. Recent research has focussed on compilation techniques, i.e., methods to alter the domain ensuring that every solution adheres to the temporally extended goals. This requires either new actions or an construction that is exponential in the size of the formula. A translation into boolean satisfiability (SAT) on the other hand requires neither. So far only one such encoding exists, which is based on the parallel $\exists$-step encoding for classical planning. We show a connection between it and recently developed compilation techniques for LTL, which may be exploited in the future. The major drawback of the encoding is that it is limited to LTL without the X operator. We show how to integrate X and describe two new encodings, which allow for more parallelism than the original encoding. An empirical evaluation shows that the new encodings outperform the current state-of-the-art encoding

Planning for temporally extended goals as propositional satisfiability

Planning for temporally extended goals (TEGs) expressed as formulae of Linear-time Temporal Logic (LTL) is a proper generalization of classical planning, not only allowing to specify properties of a goal state but of the whole plan execution. Additionall

Planning for Temporally Extended Goals as Propositional Satisfiability

Planning for temporally extended goals (TEGs) expressed as formulae of Linear-time Temporal Logic (LTL) is a proper generalization of classical planning, not only allowing to specify properties of a goal state but of the whole plan execution. Additionally, LTL formulae can be used to represent domain-specific control knowledge to speed up planning. In this paper we extend SATbased planning for LTL goals (akin to bounded LTL model-checking in verification) to partially ordered plans, thus significantly increasing planning efficiency compared to purely sequential SAT planning. We consider a very relaxed notion of partial ordering and show how planning for LTL goals (without the next-time operator) can be translated into a SAT problem and solved very efficiently. The results extend the practical applicability of SATbased planning to a wider class of planning problems. In addition, they could be applied to solving problems in bounded LTL model-checking more efficiently.

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

Generative planner for hybrid systems with temporally extended goals

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 230-237).Most unmanned missions in space and undersea are commanded by a "script" that specifies a sequence of discrete commands and continuous actions. Currently such scripts are mostly hand-generated by human operators. This introduces inefficiency, puts a significant cognitive burden on the engineers, and prevents re-planning in response to environment disturbances or plan execution failure. For discrete systems, the field of autonomy has elevated the level of commanding by developing goal-directed systems, to which human operators specify a series of temporally extended goals to be accomplished, and the goal-directed systems automatically output the correct, executable command sequences. Increasingly, the control of autonomous systems involves performing actions with a mix of discrete and continuous effects. For example, a typical autonomous underwater vehicle (AUV) mission involves discrete actions, like get GPS and take sample, and continuous actions, like descend and ascend, which are influenced by the dynamical model of the vehicle. A hybrid planner generates a sequence of discrete and continuous actions that achieve the mission goals. In this thesis, I present a novel approach to solve the generative planning problem for temporally extended goals for hybrid systems, involving both continuous and discrete actions. The planner, Kongming, incorporates two innovations. First, it employs a compact representation of all hybrid plans, called a Hybrid Flow Graph, which combines the strengths of a Planning Graph for discrete actions and Flow Tubes for continuous actions. Second, it engages novel reformulation schemes to handle temporally flexible actions and temporally extended goals. I have successfully demonstrated controlling an AUV in the Atlantic ocean using mission scripts solely generated by Kongming. I have also empirically evaluated Kongming on various real-world scenarios in the underwater domain and the air vehicle domain, and found it successfully and efficiently generates valid and optimal plans.by Hui X. Li.Ph.D

Kongming: A Generative Planner for Hybrid Systems with Temporally Extended Goals

PhD thesisMost unmanned missions in space and undersea are commanded by a "script" that specifies a sequence of discrete commands and continuous actions. Currently such scripts are mostly hand-generated by human operators. This introduces inefficiency, puts a significant cognitive burden on the engineers, and prevents re-planning in response to environment disturbances or plan execution failure. For discrete systems, the field of autonomy has elevated the level of commanding by developing goal-directed systems, to which human operators specify a series of temporally extended goals to be accomplished, and the goal-directed systems automatically output the correct, executable command sequences. Increasingly, the control of autonomous systems involves performing actions with a mix of discrete and continuous effects. For example, a typical autonomous underwater vehicle (AUV) mission involves discrete actions, like get GPS and take sample, and continuous actions, like descend and ascend, which are influenced by the dynamical model of the vehicle. A hybrid planner generates a sequence of discrete and continuous actions that achieve the mission goals. In this thesis, I present a novel approach to solve the generative planning problem for temporally extended goals for hybrid systems, involving both continuous and discrete actions. The planner, Kongming, incorporates two innovations. First, it employs a compact representation of all hybrid plans, called a Hybrid Flow Graph, which combines the strengths of a Planning Graph for discrete actions and Flow Tubes for continuous actions. Second, it engages novel reformulation schemes to handle temporally flexible actions and temporally extended goals. I have successfully demonstrated controlling an AUV in the Atlantic ocean using mission scripts solely generated by Kongming. I have also empirically evaluated Kongming on various real-world scenarios in the underwater domain and the air vehicle domain, and found it successfully and efficiently generates valid and optimal plans.Funded by the Boeing Company under contract MIT-BA-GTA-

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