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yQSS Group Inc. zQSS Group Inc. xRIACS experiment is assigned a scientific value). Different ob-servations and experiments take differing amounts of time and consume differing amounts of power and data storage.There are, in general, a number of constraints that govern the rovers activities: ffl There are time, power, data storage, and posi-tioning constraints for performing different activities. Time constraints often result from illuminationrequirement--that is, experiments may require that a target rock or sample be illuminated with a certain in-tensity, or from a certain angle.

Conformant planning is the task of generating plans given uncertainty about the initial state and action effects, and without any sensing capabilities during plan execution. The plan should be successful regardless of which particular initial world we start from. It is well known that conformant planning can be transformed into a search problem in belief space, the space whose elements are sets of possible worlds. We introduce a new representation of that search space, replacing the need to store sets of possible worlds with a need to reason about the effects of action sequences. The reasoning is done by implication tests on propositional formulas in conjunctive normal form (CNF) that capture the action sequence semantics. Based on this approach, we extend the classical heuristic forward-search planning system FF to the conformant setting. The key to this extension is an appropriate extension of the relaxation that underlies FF’s heuristic function, and of FF’s machinery for solving relaxed planning problems: the extended machinery includes a stronger form of the CNF implication tests that we use to reason about the effects of action sequences. Our experimental evaluation shows the resulting planning system to be superior to the state-of-the-art conformant planners MBP, KACMBP, and GPT in a variety of benchmark domains.

The success of model checking is largely based on its ability toe-ciently locate errors in software designs. If an error is found, a model checker produces a trail that shows how the error state can be reached, which greatly facilitates debugging. However, while current modelcheckers nd error states e ciently, the counterexamples are often unnecessarily lengthy, which hampers error explanation. This is due to the use of \naive " search algorithms in the state space exploration. In this paper we present approaches to the use of heuristic search algorithms in explicit-state model checking. We present the class of A* directed search algorithms and propose heuristics together with bitstate compression techniques for the search ofsafetyproperty violations. We achieve great reductions in the length of the error trails, and in some instances render problems analyzable by exploring a much smaller number of states than standard depth- rst search. We then suggest an improvement of the nested depth- rst search algorithm and show how it can be used together with A * to improve the search for liveness property violations. Our approach to directed explicit-state model checking has been implemented in a tool set called HSF-SPIN. We provide experimental results from the protocol validation domain using HSF-SPIN.

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.

Some recent works in conditional planning have proposed reachability heuristics to improve planner scalability, but many lack a formal description of the properties of their distance estimates. To place previous work in context and extend work on heuristics for conditional planning, we provide a formal basis for distance estimates between belief states. We give a definition for the distance between belief states that relies on aggregating underlying state distance measures. We give several techniques to aggregate state distances and their associated properties. Many existing heuristics exhibit a subset of the properties, but in order to provide a standardized comparison we present several generalizations of planning graph heuristics that are used in a single planner. We compliment our belief state distance estimate framework by also investigating efficient planning graph data structures that incorporate BDDs to compute the most effective heuristics. We developed two planners to serve as test-beds for our investigation. The first, CAltAlt, is a conformant regression planner that uses A * search. The second, POND, is a conditional progression planner that uses AO * search. We show the relative effectiveness of our heuristic techniques within these planners. We also compare the performance of these planners with several

The Model Checking Integrated Planning System (MIPS) has shown distinguished performance in the second and third international planning competitions. With its object-oriented framework architecture MIPS clearly separates the portfolio of explicit and symbolic heuristic search exploration algorithms from different on-line and off-line computed estimates and from the grounded planning problem representation.

This paper invents symbolic pattern databases (SPDB) to combine two influencing aspects for recent progress in domain-independent action planning, namely heuristic search and model checking. SPDBs are off-line computed dictionaries, generated in symbolic backward traversals of automatically inferred planning space abstractions. The entries of SPDBs serve as heuristic estimates to accelerate explicit and symbolic, approximate and optimal heuristic search planners. Selected experiments highlight that the symbolic representation yields much larger and more accurate pattern databases than the ones generated with explicit methods.

Scaling conformant planning is a problem that has received much attention of late. Many planners solve the problem as a search in the space of belief states, and some heuristic guidance techniques have been developed to estimate the distance between belief states. We claim that heuristic techniques in the past involved an ad-hoc combination of classical planning heuristics and cardinality measures. We discuss

Planning as satisfiability is a very efficient technique for classical planning, i.e., for planning domains in which both the effects of actions and the initial state are completely specified. In this paper we present C-SAT, a SAT-based procedure capable of dealing with planning domains having incomplete information about the initial state, and whose underlying transition system is specified using the highly expressive action language C. Thus, C-SAT allows for planning in domains involving (i) actions which can be executed concurrently; (ii) (ramification and qualification) constraints affecting the effects of actions; and (iii) nondeterminism in the initial state and in the effects of actions. We first prove the correctness and the completeness of C-SAT, discuss some optimizations, and then we present C-PLAN, a system based on C-SAT. C-PLAN works on any C planning problem, but some optimizations have not been fully implemented yet. Nevertheless, the experimental analysis shows that SAT-based approaches to planning with incomplete information are viable, at least in the case of problems with a high degree of parallelism.