Solving Large-Scale Planning Problems by Decomposition and Macro Generation

Asai, Masataro; Fukunaga, Alex

doi:10.1609/icaps.v25i1.13703

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…More recently, the same authors propose an approach for generating macros for ADL domain models, that includes PDDL features that are rarely supported by more traditional methods [Hofmann et al, 2020]. CAP [Asai and Fukunaga, 2015] exploits component abstraction, that allows to cluster together similar objects (introduced by MacroFF), for generating sub-goal specific macros. In other words, CAP divides complex planning problems into independent sub-problems by abstracting the components of the original problem.…”

Section: Macro-operatorsmentioning

confidence: 99%

Reformulation Techniques for Automated Planning: A Systematic Review

Alarnaouti¹,

Baryannis²,

Vallati³

2023

Preprint

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Automated planning is a prominent area of Artificial Intelligence, and an important component for intelligent autonomous agents. A cornerstone of domain-independent planning is the separation between planning logic, i.e. the automated reasoning side, and the knowledge model, that encodes a formal representation of domain knowledge needed to reason upon a given problem to synthesise a solution plan.Such a separation enables the use of reformulation techniques, which transform how a model is represented in order to improve the efficiency of plan generation.Over the past decades, significant research effort has been devoted to the design of reformulation techniques. In this paper, we present a systematic review of the large body of work on reformulation techniques for classical planning, aiming to provide a holistic view of the field and to foster future research in the area. As a tangible outcome, we provide a qualitative comparison of the existing classes of techniques, that can help researchers gain an overview of their strengths and weaknesses.

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Section: Macro-operatorsmentioning

confidence: 99%

Reformulation Techniques for Automated Planning: A Systematic Review

Alarnaouti¹,

Baryannis²,

Vallati³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…More recently, the same authors propose an approach for generating macros for ADL domain models, that includes PDDL features that are rarely supported by more traditional methods (Hofmann et al, 2020). CAP (Asai & Fukunaga, 2015) exploits component abstraction, that allows to cluster together similar objects (introduced by MacroFF), for generating sub-goal specific macros. In other words, CAP divides complex planning problems into independent subproblems by abstracting the components of the original problem.…”

Section: Macro-operatorsmentioning

confidence: 99%

“…, 2020). CAP (Asai & Fukunaga, 2015) exploits component abstraction, that allows to cluster together similar objects (introduced by MacroFF), for generating sub-goal specific macros. In other words, CAP divides complex planning problems into independent subproblems by abstracting the components of the original problem.…”

Section: Reformulation Techniques For Classical Planningmentioning

confidence: 99%

Reformulation techniques for automated planning: a systematic review

Alarnaouti,

Baryannis,

Vallati

2023

The Knowledge Engineering Review

View full text Add to dashboard Cite

Automated planning is a prominent area of Artificial Intelligence and an important component for intelligent autonomous agents. A cornerstone of domain-independent planning is the separation between planning logic, that is the automated reasoning side, and the knowledge model, that encodes a formal representation of domain knowledge needed to reason upon a given problem to synthesize a solution plan. Such a separation enables the use of reformulation techniques, which transform how a model is represented in order to improve the efficiency of plan generation. Over the past decades, significant research effort has been devoted to the design of reformulation techniques. In this paper, we present a systematic review of the large body of work on reformulation techniques for classical planning, aiming to provide a holistic view of the field and to foster future research in the area. As a tangible outcome, we provide a qualitative comparison of the existing classes of techniques, that can help researchers gain an overview of their strengths and weaknesses.

show abstract

“…Other methods, including OMA (Chrpa, Vallati, and McCluskey 2015), MacroPlanner (Jonsson 2007), and CAP (Asai and Fukunaga 2015), are semi-online. They analyze the given domain and problem instance before search commences, without using knowledge from similar domains or problems, but also without utlizing information from the search.…”

Section: Macro Actionsmentioning

confidence: 99%

Online Macro Generation for Privacy Preserving Planning

Maliah

Shani

Brafman

2016

ICAPS

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Agents that use Multi-Agent Forward Search (MAFS) todo privacy-preserving planning, often repeatedly develop similar paths. We describe a simple technique for online macro generation allowing agents to reuse successful previous action sequences. By focusing on specific sequences that end with a single public action only, we are able to address the utility problem -- our technique has negligible cost, yet provides both speedups and reduced communication in domains where agents have a reasonable amount of private actions. We describe two variants of our approach, both with attractive privacy preserving properties, and demonstrate the value of macros empirically. We also show that one variant is equivalent to secure MAFS.

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Solving Large-Scale Planning Problems by Decomposition and Macro Generation

Cited by 10 publications

References 11 publications

Reformulation Techniques for Automated Planning: A Systematic Review

Reformulation Techniques for Automated Planning: A Systematic Review

Reformulation techniques for automated planning: a systematic review

Online Macro Generation for Privacy Preserving Planning

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