An RL-trained meta-policy that uses ensemble uncertainty to choose between a cheap reactive policy and costly planning reaches goals faster than fixed baselines and adapts as the reactive policy improves.
Thinking Fast and Slow in AI
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abstract
This paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making. The premise is that if we gain insights about the causes of some human capabilities that are still lacking in AI (for instance, adaptability, generalizability, common sense, and causal reasoning), we may obtain similar capabilities in an AI system by embedding these causal components. We hope that the high-level description of our vision included in this paper, as well as the several research questions that we propose to consider, can stimulate the AI research community to define, try and evaluate new methodologies, frameworks, and evaluation metrics, in the spirit of achieving a better understanding of both human and machine intelligence.
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cs.AI 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
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When to Plan: Learning to Select Between Reactive Control and Deliberative Planning
An RL-trained meta-policy that uses ensemble uncertainty to choose between a cheap reactive policy and costly planning reaches goals faster than fixed baselines and adapts as the reactive policy improves.