An Empirical Study of the Usefulness of State-Dependent Action Costs in Planning

Corraya, Sumitra; Geißer, Florian; Speck, D. R.; Mattmüller, Robert

doi:10.1007/978-3-030-30179-8_10

Cited by 2 publications

(2 citation statements)

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“…For other algorithms, such as the computation of many goal-distance heuristics, compiling state-dependent action costs away into planning tasks with constant, i. e., stateindependent costs only, is required. Corraya et al (2019) showed that simply ignoring that costs are state-dependent can not only make the solution quality exponentially worse, but also affect search performance. Recent work Mattmüller 2015, 2016;Geißer 2018) has studied representations of state-dependent action costs as decision diagrams that are compact if additive structure in cost functions can be exploited.…”

Section: Introductionmentioning

confidence: 99%

On the Compilability and Expressive Power of State-Dependent Action Costs

Speck

Borukhson

Mattmüller

et al. 2021

ICAPS

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While state-dependent action costs are practically relevant and have been studied before, it is still unclear if they are an essential feature of planning tasks. In this paper, we study to what extent state-dependent action costs are an essential feature by analyzing under which circumstances they can be compiled away. We give a complete classification for all combinations of action cost functions and possible cost measures for the compilations. Our theoretical results show that if both task sizes and plan lengths are to be preserved polynomially, then the boundary between compilability and non-compilability lies between FP and FPSPACE computable action cost functions (under a mild assumption on the polynomial hierarchy). Preserving task sizes polynomially and plan lengths linearly at the same time is impossible.

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Section: Introductionmentioning

confidence: 99%

On the Compilability and Expressive Power of State-Dependent Action Costs

Speck

Borukhson

Mattmüller

et al. 2021

ICAPS

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“…A top-k planner also allows to "generate and test" high quality plans, which is relevant for various areas, such as goal recognition (Sohrabi, Riabov, and Udrea 2016), diverse planning (Katz and Sohrabi 2019), morally permissible planning (Lindner, Mattmüller, and Nebel 2019), or explanation generation (Eifler et al 2019). In addition, collections of plans for planning tasks can serve as practical training sets for machine learning algorithms (Toyer et al 2018;Gnad et al 2019) and enable empirical studies on properties of different planning tasks (Corraya et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Symbolic Top-k Planning

Speck

Mattmüller

Nebel

2020

AAAI

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The objective of top-k planning is to determine a set of k different plans with lowest cost for a given planning task. In practice, such a set of best plans can be preferred to a single best plan generated by ordinary optimal planners, as it allows the user to choose between different alternatives and thus take into account preferences that may be difficult to model. In this paper we show that, in general, the decision problem version of top-k planning is PSPACE-complete, as is the decision problem version of ordinary classical planning. This does not hold for polynomially bounded plans for which the decision problem turns out to be PP-hard, while the ordinary case is NP-hard. We present a novel approach to top-k planning, called sym-k, which is based on symbolic search, and prove that sym-k is sound and complete. Our empirical analysis shows that sym-k exceeds the current state of the art for both small and large k.

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An Empirical Study of the Usefulness of State-Dependent Action Costs in Planning

Cited by 2 publications

References 10 publications

On the Compilability and Expressive Power of State-Dependent Action Costs

On the Compilability and Expressive Power of State-Dependent Action Costs

Symbolic Top-k Planning

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