Learning Useful Macro-actions for Planning with N-Grams

Dulac, Adrien; Pellier, Damien; Fiorino, Humbert; Janiszek, David

doi:10.1109/ictai.2013.123

Cited by 12 publications

(13 citation statements)

References 6 publications

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“…The other method is to extract effective macro-operations through learning methods based on existing planning solutions. Dulac et al [18] used the N-grams analysis method to extract macro-operations from existing planning solutions to improve planning efficiency. With the help of a helpful action filter, only the macro-operations that are beneficial to the planning process can be selected into the enhance domain.…”

Section: Related Workmentioning

confidence: 99%

Capability Construction of C4ISR Based on AI Planning

Jiao¹,

Peiyang²,

Zhang³

et al. 2019

IEEE Access

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This paper considers a capability construction problem of the C4ISR system under serviceoriented architecture. A capability construction model is first established and described in the planning domain definition language as an artificial intelligence (AI) planning problem. To adapt the complex requirements of a C4ISR system and large scale of required services, an incremental macro-operation learning method based on n-gram analysis is proposed, and an enhanced domain is generated using a relaxation scheme. To improve the efficiency of the search algorithm, an ordered-hill-climbing (OHC) method is designed based on the length of the operations. With the above procedures, the AI planner, using macro-operation and the OHC, is presented for capability construction problems. The simulation results show that this method can effectively shorten the search time of capability construction. INDEX TERMS Artificial intelligence planning, C4ISR, capability construction, service-oriented architecture.

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Section: Related Workmentioning

confidence: 99%

Capability Construction of C4ISR Based on AI Planning

Jiao¹,

Peiyang²,

Zhang³

et al. 2019

IEEE Access

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“…Alhossaini and Beck (2013) selects problem-specific macros from a given pool of macros (hand-coded or generated by another technique). Dulac et al (2013) exploits n-gram algorithm to analyze training plans to learn macros. DBMP/S (Hofmann, Niemueller, and Lakemeyer 2017) applies Map Reduce for learning macros from a larger set of training plans.…”

Section: Related Workmentioning

confidence: 99%

Improving Domain-Independent Planning via Critical Section Macro-Operators

Chrpa

Vallati²

2019

AAAI

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Macro-operators, macros for short, are a well-known technique for enhancing performance of planning engines by providing “short-cuts” in the state space. Existing macro learning systems usually generate macros from most frequent sequences of actions in training plans. Such approach priorities frequently used sequences of actions over meaningful activities to be performed for solving planning tasks. This paper presents a technique that, inspired by resource locking in critical sections in parallel computing, learns macros capturing activities in which a limited resource (e.g., a robotic hand) is used. In particular, such macros capture the whole activity in which the resource is “locked” (e.g., the robotic hand is holding an object) and thus “bridge” states in which the resource is locked and cannot be used. We also introduce an “aggressive” variant of our technique that removes original operators superseded by macros from the domain model. Usefulness of macros is evaluated on several stateof-the-art planners, and a wide range of benchmarks from the learning tracks of the 2008 and 2011 editions of the International Planning Competition.

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“…Although Wizard achieved promising results, it is outperformed by recent techniques (Chrpa et al, 2014). Dulac et al (2013) propose techniques to estimate the length of the macro-operators, and offline techniques to select from a library of possibilities the macro-actions to be used online for a specific planning problem.…”

Section: Related Workmentioning

confidence: 99%

MEvo: a framework for effective macro sets evolution

Vallati

Chrpa

Serina

2019

Journal of Experimental & Theoretical Artificial Intelligen

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In Automated Planning, generating macro-operators (macros) is a well-known reformulation approach that is used to speed-up the planning process. Nowadays, given the number of existing techniques, a large number of macros is already available or can be easily extracted. Most of the macro generation techniques aim for using the same set of generated macros for each planner and every problem instance in a given domain. Although they provide "general improvement", the effect of macros might vary a lot for different planners. Moreover, the impact of macros on structurally different problem instances than the training ones can be potentially very detrimental. Evidently, this limits the exploitation of macros in real-world planning applications, where the structure of problem instances can often change as well as the exploited planning engine can change from time to time. In this paper we propose the Macro sets Evolution (MEvo) approach. MEvo has been designed for overcoming the aforementioned issues in order to improve the performance of domain-independent planners by dynamically selecting promising macros-taken from a given pool-while solving continuous streams of problem instances. Our extensive empirical study, involving more than 1,000 planning problem instances and 8 state-of-the-art planning engines, demonstrates effectiveness and efficiency of MEvo.

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Learning Useful Macro-actions for Planning with N-Grams

Cited by 12 publications

References 6 publications

Capability Construction of C4ISR Based on AI Planning

Capability Construction of C4ISR Based on AI Planning

Improving Domain-Independent Planning via Critical Section Macro-Operators

MEvo: a framework for effective macro sets evolution

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