maPO: An Ontology for Multi-Agent Path Finding and Its Usage for Explaining Planner Behaviour

Bharath Muppasani · Ritirupa Dey · Biplav Srivastava · Vignesh Narayanan

Artificial Intelligence Institute of South Carolina (AIISC) · University of South Carolina

Bharath Muppasani
Ritirupa Dey
Biplav Srivastava
Vignesh Narayanan
MAPF Explanations Planner-agnostic Ontology SPARQL-based
Reference Links
PURL
purl.org/ai4s/ontology/planning/multi-agent
Website
ai4society.github.io/ma-planning-ontology/
Funding. This work was supported in part by a J.P. Morgan Chase Faculty Award, AFOSR Award No. FA9550-24-1-0228, and NSF Award Nos. 2337998 and 2454027.
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maPO ontology visual
AAAI 2026 Spring Symposium Series · MAKE 2026
Machine Learning and Knowledge Engineering for Knowledge-Grounded Semantic Agents
April 7-9, 2026 · Burlingame, California
A planner-agnostic semantic framework for turning MAPF traces into provenance-aware, SPARQL-queryable explanations.