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
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…
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Taxonomy
TopicsSemantic Web and Ontologies · Logic, Reasoning, and Knowledge
MethodsOntology
