Epsilon-Safe Planning

Goldman, Robert P.; Boddy, Mark S.

doi:10.1016/b978-1-55860-332-5.50037-7

Cited by 16 publications

(12 citation statements)

References 9 publications

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“…The most well known and widely distributed POCL planner, UCPOP [107], handles operators with quantified conditional effects, along with other features of the ADL language. POCL planners have also been constructed that can handle time [130,108], metric quantities [108], and uncertainty [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%

“…[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%

“…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: Goal-directed Planningmentioning

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%

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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“…Other early works on fully-observable nondeterministic domains include the Cassandra planning system [39], CNLP [36], Plinth [18], and UCPOP [35], and QBFPlan [40]. However, all these works describe a special-purpose planning algorithm for nondeterministic planning domains, and thus, do not focus on using classical planners as a black box.…”

Section: Other Planning Techniques For Nondeterministic Planning Domainsmentioning

confidence: 99%

Plan aggregation for strong cyclic planning in nondeterministic domains

Alford

Kuter

Nau

et al. 2014

Artificial Intelligence

Self Cite

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We describe a planning algorithm, NDP2, that finds strong-cyclic solutions to nondeterministic planning problems by using a classical planner to solve a sequence of classical planning problems. NDP2 is provably correct, and fixes several problems with prior work. We also describe two preprocessing algorithms that can provide a restricted version of the symbolic abstraction capabilities of the well-known MBP planner. The preprocessing algorithms accomplish this by rewriting the planning problems, hence do not require any modifications to NDP2 or its classical planner. In our experimental comparisons of NDP2 (using FF as the classical planner) to MBP in six different planning domains, each planner outperformed the other in some domains but not others. Which planner did better depended on three things: the amount of nondeterminism in the planning domain, domain characteristics that affected how well the abstraction techniques worked, and whether the domain contained unsolvable states.

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“…Goldman and Boddy (Goldman & Boddy 1994) explore the idea of relaxing the assumption that the outcomes of different actions in a plan are probabilistically independent, using a knowledge-based model construction approach (Wellman, Breese, & Goldman 1992). This assumption is made in all the planners discussed above, although the causal influences used by Goldman and Boddy bear similarities to the external events of Weaver.…”

Section: Pointers To Other Workmentioning

confidence: 99%

An Overview of Planning Under Uncertainty

Blythe

1999

Artificial Intelligence Today

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The recent advances in computer speed and algorithms for probabilistic inference have led to a resurgence of work on planning under uncertainty. The aim is to design AI planners for environments where there may be incomplete or faulty information, where actions may not always have the same results and where there may be tradeoffs between the different possible outcomes of a plan. Addressing uncertainty in AI planning algorithms will greatly increase the range of potential applications but there is plenty of work to be done before we see practical decision-theoretic planning systems. This article outlines some of the challenges that need to be overcome and surveys some of the recent work in the area.

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Epsilon-Safe Planning

Cited by 16 publications

References 9 publications

Bridging the gap between planning and scheduling

Bridging the gap between planning and scheduling

Plan aggregation for strong cyclic planning in nondeterministic domains

An Overview of Planning Under Uncertainty

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