maPO: An Ontology for Multi-Agent Path Finding and Its Usage for Explaining Planner Behaviour - Muppasani, Bharath; Dey, Ritirupa; Srivastava, Biplav; Narayanan, Vignesh (Proceedings of the AAAI Symposium Series, 2026)
As multi-agent systems become more autonomous, particularlyin complex coordination tasks like Multi-Agent Path Finding(MAPF), the need for transparent and interpretabledecision-making becomes critical. Although execution tracesfrom MAPF algorithms provide rich diagnostic insight,existing explainability methods like visual segmentation oftrace snapshots and logic‐based “why” queries addressindividual modalities but remain fragmented. We introducethe Multi-Agent Planning Ontology (maPO), a unifiedsemantic schema that turns raw MAPF traces into a singleknowledge graph, formalizing segmentation snapshots,conflict alerts, and replanning strategies. Ourlog-to-graph pipeline ingests planner outputs as ontologyinstances, and SPARQL queries produce contrastive andlogical explanations. Our contributions are: (1) the MAPlanning Ontology schema, (2) a log-to-graph transformationpipeline and a web platform for SPARQL-based explanationgeneration, and (3) an empirical validation of theexplanation generation framework.