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<p>Ontologies are known for their ability to organize rich metadata, support the identification of novel insights via semantic queries, and promote reuse. In this paper, we build a comprehensive ontology for the problem of <i>automated planning</i>, where the objective is to find a sequence of actions that will transform a given initial state of an environment to a desired goal state. We hypothesize that the large number of available planners and diverse planning domains carry valuable information that can be leveraged to enable ontology applications, including planner selection and explanation generation. To this end, we use open data on planning domains and planners to construct the most comprehensive planning ontology to date. This construction is based on supported competency questions that cover different aspects of automated planning, such as planner capabilities, domain requirements, problem structure, plan evaluation, and explanation generation. We then demonstrate its applications in two practical use cases: planner selection and plan explanation. We have also made the ontology and associated resources available to the AI and data communities to promote further research.</p>

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Building a planning ontology to represent and exploit planning knowledge and its applications

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月26日

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  • Bharath Chandra Muppasani,
  • Nitin Gupta,
  • Vishal Pallagani,
  • Biplav Srivastava,
  • Raghava Mutharaju,
  • Michael N. Huhns,
  • Vignesh Narayanan

摘要

Ontologies are known for their ability to organize rich metadata, support the identification of novel insights via semantic queries, and promote reuse. In this paper, we build a comprehensive ontology for the problem of automated planning, where the objective is to find a sequence of actions that will transform a given initial state of an environment to a desired goal state. We hypothesize that the large number of available planners and diverse planning domains carry valuable information that can be leveraged to enable ontology applications, including planner selection and explanation generation. To this end, we use open data on planning domains and planners to construct the most comprehensive planning ontology to date. This construction is based on supported competency questions that cover different aspects of automated planning, such as planner capabilities, domain requirements, problem structure, plan evaluation, and explanation generation. We then demonstrate its applications in two practical use cases: planner selection and plan explanation. We have also made the ontology and associated resources available to the AI and data communities to promote further research.