Reformulation Techniques for Automated Planning: A Systematic Review
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866918249239674880 |
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| author | Alarnaouti, Diaeddin Baryannis, George Vallati, Mauro |
| author_facet | Alarnaouti, Diaeddin Baryannis, George Vallati, Mauro |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2301_10079 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Reformulation Techniques for Automated Planning: A Systematic Review Alarnaouti, Diaeddin Baryannis, George Vallati, Mauro Artificial Intelligence 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. |
| title | Reformulation Techniques for Automated Planning: A Systematic Review |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2301.10079 |