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Goal Identification Control Using an Information Entropy-Based Goal Uncertainty Metric.
1College of System Engineering, National University of Defense Technology, Changsha 410000, China.
Entropy (Basel, Switzerland)
|December 3, 2020
Summary
Researchers developed a new metric, relative goal uncertainty (rgu), to quantify information in agent actions. This enables controlling the speed of goal recognition in AI planning and adversarial scenarios.
Area of Science:
- Artificial Intelligence
- Game Theory
- Information Theory
Background:
- Agent goal identification can be manipulated by altering environments or exploiting agent planning.
- Understanding which actions provide less information about an agent's true goal is crucial for strategic planning.
Purpose of the Study:
- To identify actions with low information content and high uncertainty regarding an agent's goal.
- To develop methods for describing and controlling goal identification uncertainty.
- To introduce a novel measure for quantifying goal-related information in actions.
Main Methods:
- Introduced the relative goal uncertainty (rgu) measure, based on information entropy.
- Formulated goal identification control as a mixed-integer programming problem.
- Evaluated the proposed solution through empirical analysis.
Main Results:
- The relative goal uncertainty (rgu) effectively quantifies the uncertainty associated with actions.
- The proposed mixed-integer programming approach enables effective control over goal identification.
- Demonstrated the ability to control goal vagueness for single or multiple confronting agents.
Conclusions:
- The developed relative goal uncertainty (rgu) measure provides a robust way to assess action information content.
- The study offers a practical framework for manipulating goal recognition in AI systems.
- The findings are applicable to scenarios requiring strategic control over information disclosure and opponent modeling.
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