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Key to building systems that help people make better choices is inferring what people want from their behavior (Hadfield-Menell, Dragan, Abbeel, & Russell, 2016). How can this inference take place? Cognitive models, which specify how latent preferences generate behavior, could in principle be applied to this problem. By using Bayesian inference to invert such a model, we can infer preferences from behavior.
Comparing subgoal choice to theories. Having established that participants learned the task, as well as the validity of their probe responses, we now turn to our central claim, namely that subgoal choice is driven by the computational costs of hierarchical planning. Letting participants’ responses to the subgoal probes stand as reasonable proxies of subgoal choice, we relate the predictions of normative accounts and heuristics to participant probe choice across the three probes.
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