An algorithm for probabilistic planning

Kushmerick, Nicholas; Hanks, Steve; Weld, Daniel S.

doi:10.1016/0004-3702(94)00087-h

Cited by 175 publications

(135 citation statements)

References 15 publications

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“…In particular, [130,108,125] have incorporated richer models of time into the STRIPS representation, [108,85,83] allow various types of resources, and [110,47,109,87,41,116,68,124,135,67,66] introduce forms of uncertainty into the representation. [130,70,138,137] introduce more interesting types of goals, including maintenance goals and goals that involve deadlines.…”

Section: Goalsmentioning

confidence: 99%

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Bridging the gap between planning and scheduling

Smith

Frank

Jónsson

2000

The Knowledge Engineering Review

143

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Planning research in Artificial Intelligence (AI) has often focused on problems where there are cascading levels of action choice and complex interactions between actions. In contrast, Scheduling research has focused on much larger problems where there is little action choice, but the resulting ordering problem is hard. In this paper, we give an overview of AI planning and scheduling techniques, focusing on their similarities, differences, and limitations. We also argue that many difficult practical problems lie somewhere between planning and scheduling, and that neither area has the right set of tools for solving these vexing problems. The Ambitious SpacecraftImagine a hypothetical spacecraft enroute to a distant planet. Between propulsion cycles, there are time windows when the craft can be turned for communication and scientific observations. At any given time, the spacecraft has a large set of possible scientific observations that it can perform, each having some value or priority. For each observation, the spacecraft will need to be turned towards the target and the required measurement or exposure taken. Unfortunately, turning to a target is a slow operation that may take up to 30 minutes, depending on the magnitude of the turn. As a result, the choice of experiments and the order in which they are performed has a significant impact on the duration of turns and, therefore, on how much can be accomplished. All this is further complicated by several things:• There is overlap among the capabilities of instruments, so there may be a choice to make for a given observation. Naturally, the different instruments point in different directions, so the choice of instrument influences the direction and duration of the turn.• Instruments must be calibrated before use, which requires turning to one of a number of possible calibration targets. Recalibration is not required if successive observations are made with the same instrument.• Turning uses up limited fuel and observations use power. Power is limited but renewable at a rate that depends on which direction the solar panels are facing.Given all of this, the objective is to maximize scientific return for the mission or at least to use the available time wisely.Of course, this problem is not hypothetical at all. It occurs for space probes like Deep Space One, planetary rovers like Mars Sojourner, space-based observatories like the Hubble Space Telescope, airborne observatories like KAO and SO-FIA, and even automated terrestrial observatories. It is also quite similar to maintenance planning problems, where there may be a cascading set of choices for facilities, tools, and personnel, all of which affect the duration and possible ordering of various repair operations.What makes these problems particularly hard is that they are optimization problems that involve continuous time, resources, metric quantities, and a complex mixture of action choices and ordering decisions. In AI, problems involving choice of actions are often regarded as planning problems. Unfortun...

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Section: Goalsmentioning

confidence: 99%

“…[110,47,116,68,87,41,67,66]. Unfortunately, these planners have generally been unable to solve problems involving more than a handful of actions.…”

Section: Goal-directed Planningmentioning

confidence: 99%

Bridging the gap between planning and scheduling

Smith

Frank

Jónsson

2000

The Knowledge Engineering Review

143

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show abstract

“…In its ultimate form we anticipate the need for Aura to support a spectrum of task models ranging from simple invocation of applications to sophisticated models that can anticipate immediate needs of users, or even assist them in accomplishing some complex multi-step activity (like financial planning, travel assistance, or health management.) Ongoing work on Project Aura builds on research in computer-human interaction and machine learning, exploring semiautomatic learning of richer models of tasks [16,26].…”

Section: Discussion and Future Workmentioning

confidence: 99%

Aura: An Architectural Framework for User Mobility in Ubiquitous Computing Environments

Sousa

Garlan

2002

IFIP Advances in Information and Communication Technology

328

162

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“…Such uncertainty can be modeled by nondeterminism where actions may have several possible outcomes. One approach is to assume that transition probabilities are known and produce plans with a high likelihood to succeed (e.g., [12,8]). The scaleability off such planners, however, is limited due to the overhead of reasoning about probabilities.…”

Section: Introductionmentioning

confidence: 99%