Determining Redundant Actions in Sequential Plans

E, Luk #x; Chrpa,; McCluskey, T.L.; Osborne, Hugh

doi:10.1109/ictai.2012.72

Cited by 4 publications

(5 citation statements)

References 11 publications

(15 reference statements)

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“…Clearly, this is because of the IAE method is specific, i.e., only pairs or nested pairs of inverse actions are considered. Surprisingly, the runtime is high despite the complexity results (Chrpa, McCluskey, and Osborne 2012a). This can be partially explained by the fact, that IAE takes input in the PDDL format, which is much more complex to process than the SAS+ format.…”

Section: Resultsmentioning

confidence: 97%

“…Both Perfect Justification and Minimal reduction are NP-complete. Determining redundant pairs of inverse actions (inverse actions are those that revert each other's effects), which aims to eliminate the most common type of redundant actions in plans, has been also recently studied (Chrpa, McCluskey, and Osborne 2012a;2012b).…”

Section: Related Workmentioning

confidence: 99%

“…We implemented the Action Elimination (AE) algorithm as well as its greedy variant and the PMaxSAT and WPMaxSat based algorithms -minimal length reduction (MLR) and minimal reduction (MR). We used plans obtained by three state-of-the-art satisficing planners for the problems of the International Planning Competition (Coles et al 2012) and compared the algorithms with each other and with a plan optimization tool which focuses on redundant inverse actions elimination (IAE) (Chrpa, McCluskey, and Osborne 2012a).…”

Section: Experimental Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

On Different Strategies for Eliminating Redundant Actions from Plans

Balyo

Chrpa

Kilani

2021

SOCS

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Satisficing planning engines are often able to generate plans in a reasonable time, however, plans are often far from optimal. Such plans often contain a high number of redundant actions, that are actions, which can be removed without affecting the validity of the plans. Existing approaches for determining and eliminating redundant actions work in polynomial time, however, do not guarantee eliminating the "best" set of redundant actions, since such a problem is NP-complete. We introduce an approach which encodes the problem of determining the "best" set of redundant actions (i.e. having the maximum total-cost) as a weighted MaxSAT problem. Moreover, we adapt the existing polynomial technique which greedily tries to eliminate an action and its dependants from the plan in order to eliminate more expensive redundant actions. The proposed approaches are empirically compared to existing approaches on plans generated by state-of-the-art planning engines on standard planning benchmarks.

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Section: Resultsmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

Section: Experimental Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

On Different Strategies for Eliminating Redundant Actions from Plans

Balyo

Chrpa

Kilani

2021

SOCS

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“…Chrpa, McCluskey, and Osborne (2012b) proposed a technique for identifying redundant pairs of inverse actions. Later the technique was extended to consider "nested" pairs inverse actions (Chrpa, McCluskey, and Osborne 2012a). Balyo, Chrpa, and Kilani (2014) addressed the problem of finding a minimal reduction (proven to be NP-complete (Nakhost and Müller 2010b)) by compiling it to MaxSAT and have empirically shown that (G)AE often find minimal reductions.…”

Section: Related Workmentioning

confidence: 99%

On Speeding Up Methods for Identifying Redundant Actions in Plans

Med

Chrpa

2022

ICAPS

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Satisficing planning aims at generating plans that are not necessarily optimal. Often, minimising plan generation time negatively affects quality of generated plans. Acquiring plans quickly might be of critical importance in decision-making systems that operate nearly in realtime. However, (very) suboptimal plans might be expensive to execute and more prone to failures. Optimising plans after they are generated, in a spare time, can improve their quality. This paper focuses on speeding up the (Greedy) Action Elimination methods, which are used for identifying and removing redundant actions from plans in polynomial time. We present two enhancements of these methods: Plan Action Landmarks, actions that are not redundant in a given plan, and Action Cycles which are subsequences of actions which if removed do not affect the state trajectory after the last action of the cycle. We evaluate the introduced methods on benchmark problems from the Agile tracks of the International Planning Competition and on plans generated by several state-of-the-art planners, successful in the recent editions of the competition.

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“…A specific form of action reversibility involving the search for an inverse action has been investigated (Koehler and Hoffmann 2000;Chrpa, McCluskey, and Osborne 2012). The existence of a pair of inverse actions is a special case of action reversibility, where the reverse plan contains only one action.…”

Section: Introductionmentioning

confidence: 99%

Weak and Strong Reversibility of Non-deterministic Actions: Universality and Uniformity

Med,

Chrpa,

Morak

et al. 2024

ICAPS

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Classical planning looks for a sequence of actions that transform the initial state of the environment into a goal state. Studying whether the effects of an action can be undone by a sequence of other actions, that is, action reversibility, is beneficial, for example, in determining whether an action is safe to apply. This paper deals with action reversibility of non-deterministic actions, i.e., actions whose application might result in different outcomes. Inspired by the established notions of weak and strong plans in non-deterministic (or FOND) planning, we define the notions of weak and strong reversibility for non-deterministic actions. We then focus on the universality and uniformity of action reversibility, that is, whether we can always undo all possible effects of the action by the same means (i.e., policy), or whether some of the effects can never be undone. We show how these classes of problems can be solved via classical or FOND planning and evaluate our approaches on FOND benchmark domains.

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Determining Redundant Actions in Sequential Plans

Cited by 4 publications

References 11 publications

On Different Strategies for Eliminating Redundant Actions from Plans

On Different Strategies for Eliminating Redundant Actions from Plans

On Speeding Up Methods for Identifying Redundant Actions in Plans

Weak and Strong Reversibility of Non-deterministic Actions: Universality and Uniformity

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