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Intention Recognition With ProbLog.

Gary B Smith1,2,3, Vaishak Belle2,3, Ronald P A Petrick1,4

  • 1Edinburgh Centre for Robotics, Edinburgh, United Kingdom.

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Summary
This summary is machine-generated.

This study introduces a probabilistic logic programming framework to infer agent intentions for improved robot and autonomous system performance. The model accurately predicts intentions, even with incomplete environmental data.

Keywords:
assisted living at homegoal recognitionintention recognitionprobabilistic logic programmingsmart home

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Area of Science:

  • Artificial Intelligence
  • Robotics
  • Human-Robot Interaction

Background:

  • Inferring agent intentions is crucial for enhancing autonomous systems' performance and utility.
  • Smart environments require understanding occupant goals to effectively deploy assistive services.

Purpose of the Study:

  • To present a novel framework for intention reasoning using probabilistic logic programming.
  • To infer the most probable intention based on observed actions and environmental sensor data.

Main Methods:

  • Utilized ProbLog, a probabilistic extension of Prolog, for intention inference.
  • Developed a model within a smart home domain for evaluation.

Main Results:

  • Achieved 0.75 accuracy in intention inference under full observability conditions.
  • Demonstrated robustness of the model to scenarios with reduced observability.

Conclusions:

  • Probabilistic logic programming offers a viable approach for intention reasoning in autonomous systems.
  • The proposed framework shows promise for applications in smart homes and assisted living facilities.