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.

. 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...

In this article, we describe a possibilistic/probabilistic conditional planner called PTLplan. Being inspired by Bacchus and Kabanza's TLplan, PTLplan is a progressive planner that uses strategic knowledge encoded in a temporal logic to reduce its search space. Actions effects and sensing can be context dependent and uncertain, and the information the planning agent has at each point in time is represented as a set of situations with associated possibilities or probabilities. Besides presenting the planner itself -- its representation of actions and plans, and its algorithm -- we also provide some promising data from performance tests.

Contingent planning -- constructing a plan in which action selection is contingent on imperfect information received during plan execution -- can be formalized as the problem of solving a partially observable Markov decision process (POMDP). Traditional dynamic programming algorithms for POMDPs use a flat state representation that enumerates all possible states and state transitions. By contrast, AI planning algorithms use a factored state representation that supports state abstraction and allows problems with large state spaces to be represented and solved more efficiently. Boutilier and Poole (1996) have recently described how a factored state representation can be exploited by a dynamic programming algorithm for POMDPs. We extend their framework, describe an implementation and test its performance, and assess how much this approach improves the computational efficiency of dynamic programming for POMDPs. Introduction Many AI planning researchers have adopted Markov...

Partially observable Markov decision processes (POMDPs) provide a natural and principled framework to model a wide range of sequential decision making problems under uncertainty. To date, the use of POMDPs in real-world problems has been limited by the poor scalability of existing solution algorithms, which can only solve problems with up to ten thousand states. In fact, the complexity of finding an optimal policy for a finite-horizon discrete POMDP is PSPACE-complete. In practice, two important sources of intractability plague most solution algorithms: large policy spaces and large state spaces. On the other hand,

. Satisfiability problems and probabilistic models are core topics of artificial intelligence and computer science. This paper looks at the rich intersection between these two areas, opening the door for the use of satisfiability approaches in probabilistic domains. The paper examines a generic stochastic satisfiability problem, SSat, which can function for probabilistic domains as Sat does for deterministic domains. It shows the connection between SSat and well studied problems in belief network inference and planning under uncertainty, and defines algorithms, both systematic and stochastic, for solving SSat instances. These algorithms are validated on random SSat formulae generated under the fixed-clause model. In spite of the large complexity gap between SSat (PSPACE) and Sat (NP), the paper suggests that much of what we've learned about Sat transfers to the probabilistic domain. 1. Introduction There has been a recent focus in artificial intelligence (AI) on solving problems exh...

This paper proposes the problem of supply restoration in faulty power distribution systems as a benchmark for planning under uncertainty. This benchmark, which is derived from a significant realworld case, is both simple to understand and easily scalable. The goal is to reconfigure the distribution network to resupply a maximum of consumers a#ected by the faults. Due to sensor and actuator uncertainty, the location of the faulty areas and the current network configuration are only partially observable. This makes the problem very challenging. 1 Motivation The use of poor benchmarks for planning under uncertainty has often been pointed out as detrimental to the impact of the field on the wider community. Except for a few testbeds in robot navigation, see e.g. [6], we are still confined to purely artificial problems ranging from escaping the tiger behind the door to making an omelette. While well-understood toy problems are definitely useful in explaining performance di#erence...

Abstract. The past few years have seen a flurry of new approaches for planning under uncertainty, but their applicability to real-world problems is yet to be established since they have been tested only on toy benchmark problems. To fill this gap, the challenge of solving power supply restoration problems with existing planning tools has recently been issued. This requires the ability to deal with incompletely specified initial conditions, fault conditions, unpredictable action effects, and partial observability in real-time. This paper reports a first response to this nontrivial challenge, using the approach of planning via symbolic model-checking as implemented in the MBP planner. We show how the problem can be encoded in MBP’s input language, and report very promising experimental results on a number of significant test cases. 1

Symmetries are inherent in systems that consist of several interchangeable objects or components. When reasoning about such systems, big computational savings can be obtained if the presence of symmetries is recognized. In earlier work, symmetries in constraint satisfaction problems have been handled by introducing symmetry-breaking constraints.

This paper looks at the rich intersection between satisfiability problems and probabilistic models, opening the door for the use of satisfiability approaches in probabilistic domains. A generic stochastic satisfiability problem is examined, which can function for probabilistic domains as Sat does for deterministic domains. The paper defines a Davis-Putnam-Logemann-Loveland-style procedure for solving stochastic satisfiability problems, and reports on a preliminary empirical exploration of the complexity of the algorithm for a collection of randomly generated probabilistic problems. The results exhibit the familiar easyhardest -hard pattern for the difficulty of random Sat formulae. Special cases of the stochastic satisfiability problem lie in different complexity classes, and one counterintuitive result is that the computational complexity and the empirical complexity of the problems examined do not track each other exactly---problems in the hardest complexity class are not the hardes...