Extending planning graphs to an ADL subset

Koehler, Jana; Nebel, Bernhard; Hoffmann, Jörg; Dimopoulos, Yannis

doi:10.1007/3-540-63912-8_92

Cited by 120 publications

(79 citation statements)

References 5 publications

(9 reference statements)

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“…(Side note: Baum & Durdanovic (1999) also applied an alternative reinforcement learner based on the artificial economy by Holland (1985) to a simpler 3 peg blocks world problem where any disk may be placed on any other; thus the required number of moves grows only linearly with the number of disks, not exponentially; Kwee, Hutter, and Schmidhuber (2001) were able to replicate their results for n up to 5.) Traditional AI planning procedures-compare chapter V by Russell and Norvig (1994) and Koehler et al (1997)-do not learn but systematically explore all possible move combinations, using only absolutely necessary task-specific primitives (while OOPS will later use more than 70 general instructions, most of them unnecessary). On current personal computers AI planners tend to fail to solve Hanoi problem instances with n > 15 due to the exploding search space (Jana Koehler, IBM Research, personal communication, 2002).…”

Section: Towers Of Hanoimentioning

confidence: 99%

Optimal Ordered Problem Solver

2004

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Abstract. We introduce a general and in a certain sense time-optimal way of solving one problem after another, efficiently searching the space of programs that compute solution candidates, including those programs that organize and manage and adapt and reuse earlier acquired knowledge. The Optimal Ordered Problem Solver (OOPS) draws inspiration from Levin's Universal Search designed for single problems and universal Turing machines. It spends part of the total search time for a new problem on testing programs that exploit previous solution-computing programs in computable ways. If the new problem can be solved faster by copy-editing/invoking previous code than by solving the new problem from scratch, then OOPS will find this out. If not, then at least the previous solutions will not cause much harm. We introduce an efficient, recursive, backtracking-based way of implementing OOPS on realistic computers with limited storage. Experiments illustrate how OOPS can greatly profit from metalearning or metasearching, that is, searching for faster search procedures.Keywords: OOPS, bias-optimality, incremental optimal universal search, efficient planning and backtracking in program space, metasearching and metalearning, self-improvement

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Section: Towers Of Hanoimentioning

confidence: 99%

Optimal Ordered Problem Solver

2004

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“…In the recent planning systems competition held at , all but one of the competing planning systems were based on Graphplan techniques. Roughly speaking, the best planners using this technology have been limited to problems involving less than about 50 actions.As with POCL planning, the Graphplan technique has been extended to handle operators with quantified conditional effects and other features of ADL [57,86,5]. Attempts have been made to extend Graphplan to allow reasoning under uncertainty [124,135,20], but these efforts have, so far, not proven to be very practical.…”

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confidence: 99%

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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“…Thus, the testing techniques mainly employed for testing CLI programs suffer from scaling problems such as finite state machine when applied in the world of GUI's [2,3]. Therefore, it is required that a different approach is to be used for testing GUI's from what it is employed for CLI technique [4]; which in turn involves usage of a planning system [5,6]. Although employing the technique of capture-playback, functions well in CLI world but often are prone to problems which are quite significant when it is implemented for a GUI system [7].…”

Section: Introductionmentioning

confidence: 99%

Touch Free User Interface for Augmented Reality Systems

Rai

2017

Asian J Pharm Clin Res

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Augmented reality is the upcoming field of research and is often suffer from the current form of user interface. In this study, we present touch-free user interactive system for augmented reality applications to carry out multi-task operations. We validated the efficacy of the method based on the performance of several users while carrying out the complex task in our sample augmented reality game.

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Extending planning graphs to an ADL subset

Abstract: Abstract. We describe an extension of graphplan to a subset of ADL that allows conditional and universally quanti ed e ects in operators in such a w ay that almost all interesting properties of the original graphplan algorithm are preserved.

Cited by 120 publications

References 5 publications

Optimal Ordered Problem Solver

Optimal Ordered Problem Solver

Bridging the gap between planning and scheduling

Touch Free User Interface for Augmented Reality Systems

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