Future Work

Where This Goes Next

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Language Models & Ontologies

  • Ground LLM plan explanations in the ontology for context-aware, faithful narratives.
  • Natural-language SPARQL querying over planning knowledge graphs.
  • Template learning to adapt explanations to the user.
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Neuro-Symbolic Representations

  • Integrate symbolic ontology knowledge with neural planners & learners.
  • Use structured constraints (e.g., macros) to guide and explain learned policies.
  • Safety-rule checking & conflict-driven diagnostics.
Also: extend maPO to dynamic, real-world multi-agent & robotics settings (via SOSA/SSN), richer conflict taxonomies, and temporal / numeric planning.