Minimal Landmarks for Optimal Delete-Free Planning

Haslum, Patrik; Slaney, John; Thiébaux, Sylvie

doi:10.1609/icaps.v22i1.13534

Cited by 15 publications

(19 citation statements)

References 18 publications

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“…Computing h + has been investigated before using Integer Programming. Most notably, Haslum, Slaney, and Thiébaux (2012) find the value of h + by using set-inclusion minimal disjunctive landmarks and solving the IP formulation of a hitting set problem. Imai and Fukunaga (2015) compute the value of h + by solving a Mixed Integer and Linear Programming (MILP) model of delete-free planning problems.…”

Section: Related Workmentioning

confidence: 99%

“…The current efficient domain independent methods for computing h + are based on translating the delete-relaxed version of a given problem into a set of constraints and using of-the-shelf efficient constraint satisfaction solvers. One major approach of these methods is using Integer Programming (IP) (Haslum, Slaney, and Thiébaux 2012;Imai and Fukunaga 2015;Castro et al 2020).…”

Section: Introductionmentioning

confidence: 99%

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Efficient Computation and Informative Estimation of h+ by Integer and Linear Programming

Rankooh¹,

Rintanen²

2022

ICAPS

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We investigate modeling cost optimal delete-free STRIPS Planning by Integer/Linear Programming (IP/LP). We introduce two IP models and their LP relaxations based on a recently formulated representation of relaxed plans, named causal relaxed plan representation. The new models are produced by enforcing acyclicity in so-called causal relation graphs using vertex elimination and time labeling methods. We empirically show that while the vertex elimination based method outperforms the time labeling based method and all previously introduced domain independent methods for computing the exact values of h+, the time labeling based LP model is faster to solve compared to its vertex elimination based alternative, making it more suitable for using as heuristic function for optimal planning. We also theoretically analyze the admissible heuristic functions obtained by solving our LP models, and prove that the vertex elimination based heuristic is at least as informative as the time labeling based heuristic. Moreover, our empirical analysis shows that our vertex elimination based heuristic, which is a novel admissible estimation of h+, often has information complementary to that of the LM-cut heuristic.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Computation and Informative Estimation of h+ by Integer and Linear Programming

Rankooh¹,

Rintanen²

2022

ICAPS

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“…From this, we obtain analogues of the classical h max and h + heuristics for our more expressive planning formalism. To compute h + , we use the landmark-based algorithm of Haslum, Slaney and Thiébaux (2012). By changing the algorithm slightly, we can even get an analogue of the LM-Cut heuristic (Helmert and Domshlak 2009) for problems with unit-cost actions.…”

Section: Relaxed Plan Heuristicmentioning

confidence: 99%

“…Computing h + . The iterative landmark algorithm (Haslum, Slaney, and Thiébaux 2012) computes a set of disjunctive action landmarks (Karpas and Domshlak 2009) such that a minimum-cost hitting set over this collection is an optimal relaxed plan, whose cost is h + . Because this algorithm interfaces with the planning formalism only through a relaxed reachability test (is the goal relaxedreachable from the initial state using a given subset of actions?…”

Section: Optimisationsmentioning

confidence: 99%

“…), which we can perform as explained above, and because our relaxation, like the classical delete-relaxation, does not require any action more than once in an optimal relaxed plan, we can apply this algorithm to compute h + also in our setting. We use all algorithm improvements proposed by Haslum, Slaney and Thiébaux (2012); we also use the integer programming solver (Gurobi) to compute cost-optimal hitting sets.…”

Section: Optimisationsmentioning

confidence: 99%

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Optimal Planning with Global Numerical State Constraints

Ivankovic

Haslum

Thiébaux

et al. 2014

ICAPS

Self Cite

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Automating the operations of infrastructure networks such as energy grids and oil pipelines requires a range of planning and optimisation technologies. However, current planners face significant challenges in responding to this need. Notably, they are unable to model and reason about the global numerical state constraints necessary to capture flows and similar physical phenomena occurring in these networks. A single discrete control action can affect the flow throughout the network in a way that may depend on the entire network topology. Determining whether preconditions, goals and invariant conditions are satisfied requires solving a system of numerical constraints after each action application. This paper extends domain-independent optimal planning to this kind of reasoning. We present extensions of the formalism, relaxed plans, and heuristics, as well as new search variants and experimental results on two problem domains.

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Capturing (Optimal) Relaxed Plans with Stable and Supported Models of Logic Programs

Rankooh¹,

Janhunen²

2023

Theory and Practice of Logic Programming

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We establish a novel relation between delete-free planning, an important task for the AI planning community also known as relaxed planning, and logic programming. We show that given a planning problem, all subsets of actions that could be ordered to produce relaxed plans for the problem can be bijectively captured with stable models of a logic program describing the corresponding relaxed planning problem. We also consider the supported model semantics of logic programs, and introduce one causal and one diagnostic encoding of the relaxed planning problem as logic programs, both capturing relaxed plans with their supported models. Our experimental results show that these new encodings can provide major performance gain when computing optimal relaxed plans, with our diagnostic encoding outperforming state-of-the-art approaches to relaxed planning regardless of the given time limit when measured on a wide collection of STRIPS planning benchmarks.

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Minimal Landmarks for Optimal Delete-Free Planning

Cited by 15 publications

References 18 publications

Efficient Computation and Informative Estimation of h+ by Integer and Linear Programming

Efficient Computation and Informative Estimation of h+ by Integer and Linear Programming

Optimal Planning with Global Numerical State Constraints

Capturing (Optimal) Relaxed Plans with Stable and Supported Models of Logic Programs

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