Heuristics for Planning with Action Costs

Keyder, Emil; Geffner, Héctor

doi:10.1007/978-3-540-75271-4_15

Cited by 52 publications

(77 citation statements)

References 5 publications

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“…As shown by Betz and Helmert (2009), h+ is very informative but unfortunately NP-hard to compute (Bylander, 1994) and also hard to approximate (Betz & Helmert, 2009). As this relaxation does not use information which may be essential for the detection of un-solvability of the original instance (namely the destruction of fluents), a lot of research has been carried out to take some deletes into account (Fox & Long, 2001;Gerevini, Saetti & Serina, 2003;Helmert, 2004;Helmert & Geffner, 2008;Keyder & Geffner, 2008;Cai, Hoffmann & Helmert, 2009). Another recent approach (Haslum, Slaney & Thiébaux, 2012;Keyder, Hoffmann & Haslum, 2012) consists in enriching the classical relaxation with a set of fact conjunctions.…”

Section: Temporal Relaxationmentioning

confidence: 99%

Monotone Temporal Planning: Tractability, Extensions and Applications

Cooper¹,

Maris²,

Régnier³

2014

jair

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This paper describes a polynomially-solvable class of temporal planning problems. Polynomiality follows from two assumptions. Firstly, by supposing that each sub-goal fluent can be established by at most one action, we can quickly determine which actions are necessary in any plan. Secondly, the monotonicity of sub-goal fluents allows us to express planning as an instance of STP ≠ (Simple Temporal Problem with difference constraints). This class includes temporally-expressive problems requiring the concurrent execution of actions, with potential applications in the chemical, pharmaceutical and construction industries.We also show that any (temporal) planning problem has a monotone relaxation which can lead to the polynomial-time detection of its unsolvability in certain cases. Indeed we show that our relaxation is orthogonal to relaxations based on the ignore-deletes approach used in classical planning since it preserves deletes and can also exploit temporal information.

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Section: Temporal Relaxationmentioning

confidence: 99%

Monotone Temporal Planning: Tractability, Extensions and Applications

Cooper¹,

Maris²,

Régnier³

2014

jair

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“…Most planners minimizing the total action costs use heuristic state space search, such as LAMA [Richter and Westphal 2008;2010], HSP * 0 , HSP * F [Haslum 2008], FF(h a ) [Keyder and Geffner 2008], CO-PLAN . Research has been carried out on optimizing the number of actions [Vidal and Geffner 2004;Haslum and Geffner 2000;Bttner and Rintanen 2005;Helmert et al 2008], which is a special case of optimizing the total action costs.…”

Section: Cost Sensitive Plannersmentioning

confidence: 99%

A SAT-based approach to cost-sensitive temporally expressive planning

Lü

Huang

Chen

et al. 2013

ACM Trans. Intell. Syst. Technol.

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Complex features, such as temporal dependencies and numerical cost constraints, are hallmarks of real-world planning problems. In this paper, we consider the challenging problem of cost-sensitive temporally expressive (CSTE) planning, which requires concurrency of durative actions and optimization of action costs. We first propose a scheme to translate a CSTE planning problem to a minimum cost (MinCost) satisfiability (SAT) problem and to integrate with a relaxed parallel planning semantics for handling true temporal expressiveness. Our scheme finds solution plans that optimize temporal makespan, and also minimize total action costs at the optimal makespan. We propose two approaches for solving MinCost SAT. The first is based on a transformation of a MinCost SAT problem to a weighted partial Max-SAT (WPMax-SAT), and the second, called BB-CDCL, is an integration of the branch-and-bound technique and the conflict driven clause learning (CDCL) method. We also develop a CSTE customized variable branching scheme for BB-CDCL which can significantly improve the search efficiency. Our experiments on the existing CSTE benchmark domains show that our planner compares favorably to the state-of-the-art temporally expressive planners in both efficiency and quality.

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“…A plan π + (s) for the relaxation P + can be obtained from the heuristic h add (s) by simply collecting the best supports recursively backwards from the goal [21]. This is actually the technique used in the state-of-the-art planner LAMA [18], winner of the 2008 International Planning Competition [15], that defines the heuristic h(s) as the cost of this 'relaxed plan', and uses it in problems where action costs are not uniform.…”

Section: Classical Planningmentioning

confidence: 99%

Inference and Learning in Planning

Geffner

2012

Inductive Logic Programming

Self Cite

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Abstract. Planning is concerned with the development of solvers for a wide range of models where actions must be selected for achieving goals. In these models, actions may be deterministic or not, and full or partial sensing may be available. In the last few years, significant progress has been made, resulting in algorithms that can produce plans effectively in a variety of settings. These developments have to do with the formulation and use of general inference techniques and transformations. In this invited talk, I'll review the inference techniques used for solving individual planning instances from scratch, and discuss the use of learning methods and transformations for obtaining more general solutions.

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Heuristics for Planning with Action Costs

Cited by 52 publications

References 5 publications

Monotone Temporal Planning: Tractability, Extensions and Applications

Monotone Temporal Planning: Tractability, Extensions and Applications

A SAT-based approach to cost-sensitive temporally expressive planning

Inference and Learning in Planning

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