A policy-based RL agent plays a 20 questions game to recommend optimal cybersecurity education and explain the decision by eliciting the minimal set of evidential facts needed to justify defensive actions.
Mitigating the Information Cocoon Effect in Cognitively Aligned Recommendations: A Human -Centered Approach
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Learning-to-Explain through 20Q Gaming: An Explainable Recommender for Cybersecurity Education
A policy-based RL agent plays a 20 questions game to recommend optimal cybersecurity education and explain the decision by eliciting the minimal set of evidential facts needed to justify defensive actions.