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

Abstract

As multi-agent systems become more autonomous, particularly in complex coordination tasks like Multi-Agent Path Finding (MAPF), the need for transparent and interpretable decision-making becomes critical. Although execution traces from MAPF algorithms provide rich diagnostic insight, existing explainability methods like visual segmentation of trace snapshots and logic‐based “why” queries address individual modalities but remain fragmented. We introduce the Multi-Agent Planning Ontology (maPO), a unified semantic schema that turns raw MAPF traces into a single knowledge graph, formalizing segmentation snapshots, conflict alerts, and replanning strategies. Our log-to-graph pipeline ingests planner outputs as ontology instances, and SPARQL queries produce contrastive and logical explanations. Our contributions are: (1) the MA Planning Ontology schema, (2) a log-to-graph transformation pipeline and a web platform for SPARQL-based explanation generation, and (3) an empirical validation of the explanation generation framework.