Operator Mutexes and Symmetries for Simplifying Planning Tasks

Fišer, Daniel; Torralba, Álvaro; Shleyfman, Alexander

doi:10.1609/aaai.v33i01.33017586

Cited by 8 publications

(9 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So, we were also wondering whether removing redundant operators using endomorphisms can expose symmetries (on the so-called Problem Description Graph (Pochter, Zohar, and Rosenschein 2011)) that could not be found otherwise. As can be seen in Table 1, this behaviour is actually quite common, which is a promising result considering other planning methods depending on structural symmetries of planning tasks (e.g., Sievers et al 2015;Shleyfman et al 2015;Gnad et al 2017;Fišer, Torralba, and Shleyfman 2019). pruning with h 2 and endomorphisms.…”

Section: Experimental Evaluationmentioning

confidence: 67%

“…Another related method is the combination of operator mutexes (op-mutexes) and symmetries proposed by Fišer, Torralba, and Shleyfman (2019). The main idea behind this technique is based on the observation that if two operators cannot co-occur in the same optimal plan (i.e., they form an op-mutex) and there is a symmetry (stabilized for both the initial state and goals) mapping one operator to another, then one of the operators can be safely removed.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, Fišer, Torralba, and Shleyfman (2019) proposed a technique combining so-called operator mutexes (op-mutexes) and symmetries in such a way that allows to identify operators that are redundant in a sense that removing them still preserves at least one (optimal) plan. The main idea behind this technique is based on the observation that if two operators cannot occur simultaneously in an optimal plan and there is a symmetry mapping one operator to another, then one of the operators can be safely removed.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Endomorphisms of Classical Planning Tasks

Horčı́k

Fišer

2021

AAAI

Self Cite

View full text Add to dashboard Cite

Detection of redundant operators that can be safely removed from the planning task is an essential technique allowing to greatly improve performance of planners. In this paper, we employ structure-preserving maps on labeled transition systems (LTSs), namely endomorphisms well known from model theory, in order to detect redundancy. Computing endomorphisms of an LTS induced by a planning task is typically infeasible, so we show how to compute some of them on concise representations of planning tasks such as finite domain representations and factored LTSs. We formulate the computation of endomorphisms as a constraint satisfaction problem (CSP) that can be solved by an off-the-shelf CSP solver. Finally, we experimentally verify that the proposed method can find a sizeable number of redundant operators on the standard benchmark set.

show abstract

Section: Experimental Evaluationmentioning

confidence: 67%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Endomorphisms of Classical Planning Tasks

Horčı́k

Fišer

2021

AAAI

Self Cite

View full text Add to dashboard Cite

show abstract

“…This happens in some domains of the IPC benchmark set for optimal planning. For example, Fišer, Torralba, and Shleyfman (2019) write that "In Childsnack, [they] measured about twice as many expanded states per second. However, no planner solved any instance in this domain.".…”

Section: Smooth Scalingmentioning

confidence: 99%

Automatic Instance Generation for Classical Planning

Torralba

Seipp

Sievers

2021

ICAPS

Self Cite

View full text Add to dashboard Cite

The benchmarks from previous International Planning Competitions (IPCs) are the de-facto standard for evaluating planning algorithms. The IPC set is both a collection of planning domains and a selection of instances from these domains. Most of the domains come with a parameterized generator that generates new instances for a given set of parameter values. Due to the steady progress of planning research some of the instances that were generated for past IPCs are inadequate for evaluating current planners. To alleviate this problem, we introduce Autoscale, an automatic tool that selects instances for a given domain. Autoscale takes into account constraints from the domain designer as well as the performance of current planners to generate an instance set of appropriate difficulty, while avoiding too much bias with respect to the considered planners. We show that the resulting benchmark set is superior to the IPC set and has the potential of improving empirical evaluation of planning research.

show abstract

“…In this work we concentrate on symmetry breaking in FDR planning. Research on this topic has been intensive and has led to numerous results (Starke, 1991;Emerson & Sistla, 1996;Fox & Long, 1999;Rintanen, 2003;Pochter, Zohar, & Rosenschein, 2011;Domshlak, Katz, & Shleyfman, 2012;Gnad, Torralba, Shleyfman, & Hoffmann, 2017;Fišer, Torralba, & Shleyfman, 2019). These results rely on the computation of a certain kind of symmetries of the state transition graph known as automorphisms.…”

Section: Introductionmentioning

confidence: 99%

Computational Complexity of Computing Symmetries in Finite-Domain Planning

Shleyfman

Jönsson²

2021

jair

Self Cite

View full text Add to dashboard Cite

Symmetry-based pruning is a powerful method for reducing the search effort in finitedomain planning. This method is based on exploiting an automorphism group connected to the ground description of the planning task { these automorphisms are known as structural symmetries. In particular, we are interested in the StructSym problem where the generators of this group are to be computed. It has been observed in practice that the StructSym problem is surprisingly easy to solve. We explain this phenomenon by showing that StructSym is GI-complete, i.e., the graph isomorphism problem is polynomial-time equivalent to it and, consequently, solvable in quasi-polynomial time. This implies that it is solvable substantially faster than most computationally hard problems encountered in AI. We accompany this result by identifying natural restrictions of the planning task and its causal graph that ensure that StructSym can be solved in polynomial time. Given that the StructSym problem is GI-complete and thus solvable quite efficiently, it is interesting to analyse if other symmetries (than those that are encompassed by the StructSym problem) can be computed and/or analysed efficiently, too. To this end, we present a highly negative result: checking whether there exists an automorphism of the state transition graph that maps one state s into another state t is a PSPACE-hard problem and, consequently, at least as hard as the planning problem itself.

show abstract

Operator Mutexes and Symmetries for Simplifying Planning Tasks

Cited by 8 publications

References 7 publications

Endomorphisms of Classical Planning Tasks

Endomorphisms of Classical Planning Tasks

Automatic Instance Generation for Classical Planning

Computational Complexity of Computing Symmetries in Finite-Domain Planning

Contact Info

Product

Resources

About