Many planning domains require a richer notion of time in which actions can overlap and have different durations. The key to fast performance in classical planners (e.g., Graphplan, ipp, and Blackbox) has been the use of a disjunctive representation with powerful mutual exclusion reasoning. This paper presents TGP a new algorithm for temporal planning. TGP operates

Planning under partial observability is one of the most significant and challenging planning problems. It has been

The past five years have seen dramatic advances in planning algorithms, with an emphasis on propositional methods such as Graphplan and compilers that convert planning problems into propositional CNF formulae for solution via systematic or stochastic SAT methods. Related work on the Deep Space One spacecraft control algorithms advances our understanding of interleaved planning and execution. In this survey,we explain the latest techniques and suggest areas for future research.

In this paper we present a new approach to the problem of planning with incomplete information and sensing. Our approach is based on a higher level, "knowledge-based," representation of the planner's knowledge and of the domain actions. In particular, in our approach we use a set of formulae from a first-order modal logic of knowledge to represent the planner's incomplete knowledge state. Actions are then represented as updates to this collection of formulae. Hence, actions are being modelled in terms of how they modify the knowledge state of the planner rather than in terms of how they modify the physical world. We have constructed a planner to utilize this representation and we use it to show that on many common problems this more abstract representation is perfectly adequate for solving the planning problem, and that in fact it scales better and supports features that make it applicable to much richer domains and problems.

In Part I of this series of papers, we have proposed a new logic-based planning language, called K. This language facilitates the description of transitions between states of knowledge and it is well suited for planning under incomplete knowledge. Nonetheless, K also supports the representation of transitions between states of the world (i.e., states of complete knowledge) as a special case, proving to be very flexible. In the present Part II, we describe the DLV planning system, which implements K on top of the disjunctive logic programming system DLV. This novel planning system allows for solving hard planning problems, including secure planning under incomplete initial states (often called conformant planning in the literature), which cannot be solved at all by other logic-based planning systems such as traditional satisfiability planners. We present a detailed comparison of the system to several state-of-the-art conformant planning systems, both at the level of system features and on benchmark problems. Our results indicate that, thanks to the power of knowledge-state problem encoding, the DLV system is competitive even with special purpose conformant planning systems, and it often supplies a more natural and simple representation of the planning problems.

. The Graphplan planner has enjoyed considerable success as a planning algorithm for classical STRIPS domains. In this paper we explore the extent to which its representation can be used for probabilistic planning. In particular, we consider an MDP-style framework in which the state of the world is known but actions are probabilistic, and the objective is to produce a finite horizon contingent plan with highest probability of success within the horizon. We describe two extensions of Graphplan in this direction. The first, PGraphplan, produces an optimal contingent plan. It typically suffers a performance hit compared to Graphplan but still appears to be fast compared with other approaches to probabilistic planning problems. The second, TGraphplan, runs at essentially the same speed as Graphplan, but produces potentially sub-optimal policies: TGraphplan's policy selects the first action on the highest probability trajectory from its current state to the goal. Ideally, we would like an...

Most recent strides in scaling up planning have centered around two competing themes--disjunctive planners, exemplified by Graphplan, and heuristic state search planners, exemplified by UNPOP, HSP and HSP-r. In this paper, we present a novel approach for successfully harnessing the advantages of the two competing paradigms to develop planners that are significantly more powerful than either of the approaches. Specifically, we show that the polynomial-time planning graph structure that the Graphplan builds provides a rich substrate for deriving a family of highly effective heuristics for guiding state space search as well as CSP-style search. The main leverage provided by the planning graph structure is a systematic and graded way to take subgoal interactions into account in designing state space heuristics.

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Several recent papers describe algorithms for generating conditional and/or probabilistic plans. In this paper, we synthesize this work, and present a unifying algorithm that incorporates and clarifies the main techniques that have been developed in the previous literature. Our algorithm decouples the search-control strategy for conditional and/or probabilistic planning from the underlying plan-refinement process. A similar decoupling has proven to be very useful in the analysis of classical planning algorithms, and we show that it can be at least as useful here, where the searchcontrol decisions are even more crucial. Previous probabilistic /conditional planners have been severely limited by the fact that they do not know how to handle failure points to advantage. We show how a principled selection of failure points can be performed within the framework our algorithm. We also describe and show the effectiveness of additional heuristics. We describe our implemented system called Mahinu...

The Model Based Planner (MBP) is a system for planning in non-deterministic domains. It can generate plans automatically to solve various planning problems, like conformant planning, planning under partial observability, and planning for temporally extended goals. Moreover, MBP can validate plans, and offers a variety of simulation functionalities for plans and domains. MBP is based on Symbolic Model Checking techniques, and Binary Decision Diagrams (BDDs), that provide a practical solution to the problem of dealing with the large size of realistic planning problems. Experimental analysis in the course of the last years has shown MBP to be state-of-the-art in planning for nondeterministic domains. The demo aims at showing MBP’s array of functionalities for plan generation, validation and simulation over an increasingly complex navigation problem.