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A Planning Ontology to Represent and Exploit Planning Knowledge for Performance Efficiency

Bharath Muppasani, Vishal Pallagani, Biplav Srivastava, Raghava Mutharaju, Michael N. Huhns, Vignesh Narayanan

TL;DR

The paper addresses the challenge of selecting effective planners and tuning planning processes by introducing a planning ontology built from IPC benchmarks. It formalizes planning as $(S,A,T,I,G)$ and grounds four interconnected concepts—Domain, Problem, Plan, and Planner—to enable structured reasoning, competency-question guided design, and data-driven use cases. The authors demonstrate two practical applications: (i) identifying promising planners for a domain using IPC data and ontology-guided policies, and (ii) extracting macro-operators from plan statistics to boost planner efficiency, with results showing improvements in several domains but domain-dependent effects. The ontology is released under FAIR principles with open resources, SPARQL examples, and a knowledge-graph workflow, facilitating reuse and extension by the planning community. This work advances the integration of ontologies and AI planning, offering a scalable framework for planner selection, macro learning, and knowledge sharing, and suggesting future exploration of hybrid reasoning with large language models.

Abstract

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 consider the problem of automated planning, where the objective is to find a sequence of actions that will move an agent from an initial state of the world to a desired goal state. We hypothesize that given a large number of available planners and diverse planning domains; they carry essential information that can be leveraged to identify suitable planners and improve their performance for a domain. We use data on planning domains and planners from the International Planning Competition (IPC) to construct a planning ontology and demonstrate via experiments in two use cases that the ontology can lead to the selection of promising planners and improving their performance using macros - a form of action ordering constraints extracted from planning ontology. We also make the planning ontology and associated resources available to the community to promote further research.

A Planning Ontology to Represent and Exploit Planning Knowledge for Performance Efficiency

TL;DR

The paper addresses the challenge of selecting effective planners and tuning planning processes by introducing a planning ontology built from IPC benchmarks. It formalizes planning as and grounds four interconnected concepts—Domain, Problem, Plan, and Planner—to enable structured reasoning, competency-question guided design, and data-driven use cases. The authors demonstrate two practical applications: (i) identifying promising planners for a domain using IPC data and ontology-guided policies, and (ii) extracting macro-operators from plan statistics to boost planner efficiency, with results showing improvements in several domains but domain-dependent effects. The ontology is released under FAIR principles with open resources, SPARQL examples, and a knowledge-graph workflow, facilitating reuse and extension by the planning community. This work advances the integration of ontologies and AI planning, offering a scalable framework for planner selection, macro learning, and knowledge sharing, and suggesting future exploration of hybrid reasoning with large language models.

Abstract

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 consider the problem of automated planning, where the objective is to find a sequence of actions that will move an agent from an initial state of the world to a desired goal state. We hypothesize that given a large number of available planners and diverse planning domains; they carry essential information that can be leveraged to identify suitable planners and improve their performance for a domain. We use data on planning domains and planners from the International Planning Competition (IPC) to construct a planning ontology and demonstrate via experiments in two use cases that the ontology can lead to the selection of promising planners and improving their performance using macros - a form of action ordering constraints extracted from planning ontology. We also make the planning ontology and associated resources available to the community to promote further research.
Paper Structure (18 sections, 14 equations, 4 figures, 4 tables)

This paper contains 18 sections, 14 equations, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Automated Planning
  4. International Planning Competition (IPC)
  5. Related Work
  6. Planning Ontology
  7. Competency Questions
  8. Design
  9. Domain
  10. Problem
  11. Plan
  12. Planner
  13. Accessing Planning Ontology
  14. Usage of Planning Ontology
  15. Evaluation of Competency questions:
  16. ...and 3 more sections

Figures (4)

  • Figure 1: Demonstration of automated planning problem with blocksworld domain example
  • Figure 2: An illustrative overview of the planning ontology, segmented into categories that encapsulate the core concepts of automated planning: domain, problem, plan, and planner performance. Each category is distinctly represented by colored rectangles. Classes with thick outlines denote concepts that have been adapted or reused from existing ontologies.
  • Figure 3: Workflow diagram illustrating the integration of the Planning Ontology with AI Planning system, which supports use cases like Macro Operators Extraction and Planner Selection, ultimately enabling the interactive plan explanations with the help of generated Knowledge graph.
  • Figure 4: Knowledge graph representation for blocksworld domain from IPC-2000