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  3. Supporting Human-agent Communication For Explainable Planning In Spatial-temporal Planning Problems.
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  3. Supporting Human-agent Communication For Explainable Planning In Spatial-temporal Planning Problems.

Related Experiment Videos

Supporting human-agent communication for explainable planning in spatial-temporal planning problems.

Alan Lindsay1, Andrés A Ramírez-Duque1, Bart Craenen1

  • 1Heriot-Watt University, Edinburgh, EH14 4AS UK.

Neural Computing & Applications
|May 11, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study enhances plan explainability for underwater autonomous vehicles by introducing a multi-agent spatial-temporal (MAST) structure. This allows operators to better query and understand mission plans, improving human-agent communication.

Keywords:
Automatic model extensionsAutonomous vehiclesExplainable planningHuman-agent communicationTransferable knowledge

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Robotics
  • Human-Computer Interaction

Background:

  • Automated planning requires operators to understand and explore generated plans.
  • Underwater autonomous vehicle missions present unique challenges for plan explainability.
  • Existing planning models may not directly map to intuitive query concepts like distance or duration.

Purpose of the Study:

  • To improve plan explainability and plan space exploration for underwater autonomous vehicle missions.
  • To develop a framework that bridges the gap between planning model components and user-understandable concepts.
  • To enable more effective user guidance and communication in complex mission scenarios.

Main Methods:

  • Focused on the multi-agent spatial-temporal (MAST) structure as a key substructure.
  • Defined model extensions incorporating concepts relevant to the MAST structure.
  • Developed and evaluated new query types, including those based on numeric functions, utilizing the extended model.
  • Conducted empirical and qualitative user studies in target and benchmark domains.

    • Main Results:

    • Demonstrated the utility of the MAST structure in query formulation.
    • Showcased new query types that leverage extended model concepts for better user interaction.
    • Empirical studies validated the effectiveness of the new structure and query types.
    • Qualitative studies confirmed improved user communication and understanding of mission objectives.

    Conclusions:

    • The extended MAST structure significantly enhances plan explainability for underwater autonomous vehicles.
    • New query types enable users to better communicate intent and shape mission objectives.
    • The approach supports more relevant information for agent responses and explanations, improving human-agent collaboration.