Conditional Nonlinear Planning

Peot, Mark A.; Smith, David E.

doi:10.1016/b978-0-08-049944-4.50027-6

Cited by 115 publications

(69 citation statements)

References 4 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%

“…[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%

“…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%

See 2 more Smart Citations

Bridging the gap between planning and scheduling

Smith

Frank

Jónsson

2000

The Knowledge Engineering Review

Self Cite

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%

Section: Goal-directed Planningmentioning

confidence: 99%

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Bridging the gap between planning and scheduling

Smith

Frank

Jónsson

2000

The Knowledge Engineering Review

Self Cite

143

View full text Add to dashboard Cite

show abstract

“…Conditional planning is concerned with reaching goals from an initial state, in the presence of incomplete knowledge and sensing actions (Warren 1976;Peot and Smith 1992;Pryor and Collins 1996). Since all contingencies are considered in advance, a conditional plan is essentially a tree of actions where the root represents the initial state, leaves represent goal states, and each branch of the tree from the root to a leaf represents a possible execution of actuation actions and sensing actions to reach a goal state.…”

Section: Introductionmentioning

confidence: 99%

Hybrid conditional planning using answer set programming

Yalciner¹,

Nouman²,

Patoğlu³

et al. 2017

Theory and Practice of Logic Programming

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We introduce a parallel offline algorithm for computing hybrid conditional plans, called HCP-ASP, oriented towards robotics applications. HCP-ASP relies on modeling actuation actions and sensing actions in an expressive nonmonotonic language of answer set programming (ASP), and computation of the branches of a conditional plan in parallel using an ASP solver. In particular, thanks to external atoms, continuous feasibility checks (like collision checks) are embedded into formal representations of actuation actions and sensing actions in ASP; and thus each branch of a hybrid conditional plan describes a feasible execution of actions to reach their goals. Utilizing nonmonotonic constructs and nondeterministic choices, partial knowledge about states and nondeterministic effects of sensing actions can be explicitly formalized in ASP; and thus each branch of a conditional plan can be computed by an ASP solver without necessitating a conformant planner and an ordering of sensing actions in advance. We apply our method in a service robotics domain and report experimental evaluations. Furthermore, we present performance comparisons with other compilation based conditional planners on standardized benchmark domains. This paper is under consideration for acceptance in TPLP.

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“…Contingent planning is concerned with the problem of achieving goals in the presence of incomplete information and sensing actions [1,2]. This is one of the most general problems considered in the area of planning and one of the hardest [3,4].…”

Section: Introductionmentioning

confidence: 99%

Fast and Informed Action Selection for Planning with Sensing

Albore

Palacios

Geffner

2007

Current Topics in Artificial Intelligence

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Abstract. Consider a robot whose task is to pick up some colored balls from a grid, taking the red balls to a red spot, the blue balls to a blue spot and so on, one by one, without knowing either the location or color of the balls but having a sensor that can find out both when a ball is near. This problem is simple and can be solved by a domain-independent contingent planner in principle, but in practice this is not possible: the size of any valid plan constructed by a contingent planner is exponential in the number of observations which in these problems is very large. This doesn't mean that planning techniques are of no use for these problems but that building or verifying complete contingent plans is not feasible in general. In this work, we develop a domain-independent action selection mechanism that does not build full contingent plans but just chooses the action to do next in a closed-loop fashion. For this to work, however, the mechanism must be both fast and informed. We take advantage of recent ideas that allow delete and precondition-free contingent problems to be converted into conformant problems, and conformant problems into classical ones, for mapping the action selection problem in contingent planning into an action selection problem in classical planning that takes sensing actions into account. The formulation is tested over standard contingent planning benchmarks and problems that require plans of exponential size.

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Conditional Nonlinear Planning

Cited by 115 publications

References 4 publications

Bridging the gap between planning and scheduling

Bridging the gap between planning and scheduling

Hybrid conditional planning using answer set programming

Fast and Informed Action Selection for Planning with Sensing

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