Learning from Errors by Counterfactual Reasoning in a Unified Cognitive Architecture


A key characteristic of human cognition is the ability to learn from undesirable outcomes. This paper presents a computational account of learning from errors based on counterfactual reasoning, which we embed in Icarus, a unified theory of the cognitive architecture. Our approach acquires new skills from single experiences that improve upon and mask those that initially produced the undesirable behavior. We demonstrate the operation of this model in a simulated urban driving environment. We also relate our approach to other research on error-driven learning and discuss possible improvements to the framework.

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