Planning Ontology Foundation
Initial ontology schema for planning domains, planners, tasks, and performance properties.
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Planning Ontology & Explainability
Ontology-driven representations for planning knowledge and explainability, spanning PO, maPO, and OMEGA for queryable, reusable, and human-readable planner behavior.
CODS 2024
Discover Data 2025
AAAI-MAKE 2026
AAAI 2026 Demo
95.2%
User preference over baseline explanations
4.40/5
OMEGA clarity rating
94.39%
Competency-question coverage
4
Published milestones from 2024 to 2026
Overview
This research line builds ontological infrastructure for automated planning and explainability. It starts with a general planning ontology (PO) for planner behavior, expands into multi-agent execution semantics (maPO), and culminates in OMEGA, an explainability pipeline that converts raw MAPF traces into human-readable causal narratives.
The unifying goal is to make planning artifacts queryable and reusable: selecting planners from historical signals, extracting reusable macro patterns, and explaining behavior in robot-deployment settings where standard solver logs are hard to interpret.
Expanded PO + Applications
Extended ontology coverage and evidence on planner selection and planning knowledge reuse.
maPO for MAPF
Ontology extension for collision events, replanning causes, and multi-agent execution semantics.
OMEGA Explainability Tool
Operational system that maps MAPF execution logs to contextual, user-facing explanations.
Core Thesis: planning ontologies are not just metadata repositories; they are decision-support layers that improve planner choice, execution understanding, and human trust.
Planning Ontology (PO)
PO Project ↗
Key Idea: PO is an OWL 2 ontology that organizes planning domains, solver properties, run-time outcomes, and
PROV-O provenance into a SPARQL-queryable knowledge graph.
Historical IPC traces become structured evidence for selecting planners that best match domain and instance characteristics before execution.
Provenance-rich action traces support mining reusable macro operators that reduce search burden in related planning tasks.
A Planning.Domains plugin converts PDDL artifacts to RDF, enabling direct ontology-backed analysis in existing planning workflows.
Multi-Agent Planning Ontology (maPO) & OMEGA
maPO Project ↗
maPO & OMEGA: maPO models MAPF-specific execution semantics including agent conflicts, collision events, and replanning context. OMEGA uses this schema to convert low-level trace logs into causal, user-readable explanations.
Key entities include ma:Agent, ma:CollisionEvent, ma:ConflictAlert, and ma:ReplanningStrategy, connected through provenance links that preserve event causality.
OMEGA Framework: execution traces are transformed to RDF graphs over maPO, queried through SPARQL templates, and rendered as contextual explanations aligned with animation states. In user studies, participants preferred OMEGA explanations over raw logs (95.2% preference; 4.40/5 clarity).
System Demonstration
Watch Demo
The demo emphasizes practical explainability during MAPF execution, showing why collisions occur, how conflict alerts are triggered, and which replanning strategy resolves each issue.
Selected Publications
The publications below trace the progression from planning ontology foundations to MAPF-specific explanation tooling. Each milestone adds a more concrete execution or explanation layer on top of the same semantic base.
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AAAI 2026 Demo
OMEGA: An Ontology-Driven Tool for Explaining Multi-Agent Path Finding
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AAAI-MAKE 2026
maPO: An Ontology for Multi-Agent Path Finding and Its Usage for Explaining Planner Behaviour
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Discover Data 2025
Building a Plan Ontology to Represent and Exploit Planning Knowledge and Its Applications
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CODS 2024
Building a Plan Ontology to Represent and Exploit Planning Knowledge