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

TL;DR

This paper introduces a planning ontology that organizes planning knowledge to enhance planner selection and performance, leveraging data from IPC to demonstrate improved planning efficiency through semantic reasoning and macro extraction.

Contribution

The paper presents a novel ontology for automated planning that enables better planner selection and performance optimization using semantic knowledge and macro constraints.

Findings

Ontology improves planner selection accuracy

Macros derived from ontology enhance planning efficiency

Open resources promote further research in planning

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.