Propose to Learn, Learn to Propose: Evaluability-Aware Assistance under Bounded Rationality

arXiv:2609.02242 · cs.AI, cs.HC, cs.MA · Submitted 2026-09-02 · Read on arXiv

cs.AI, cs.HC, cs.MA

Submitted: 2026-09-02

Updated: 2026-09-02

Terminology

• Ablation — 15× in this paper · explained in 1 episode
• Fisher Information — 9× in this paper · explained in 3 episodes
• Reinforcement Learning — 7× in this paper · explained in 4 episodes
• Alignment — 3× in this paper · explained in 1 episode
• Reward Function — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Action Space — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Cognitive Load — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Adaptive Lookahead — 1× in this paper · explained in 1 episode
• Adaptive Planning — 1× in this paper · explained in 1 episode
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Bayesian Optimization — 1× in this paper · explained in 3 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Information Gain — 1× in this paper · explained in 2 episodes
• Nadia — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes

Sources

• Asking Easy Questions: A User-Friendly Approach to Active Reward Learning
• Information-Theoretic Bounded Rationality
• Shared Autonomy via Deep Reinforcement Learning
• Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind
• Eliciting Human Preferences with Language Models

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