When Does Bounded-Optimal Metareasoning Favor Few Cognitive Systems?
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Published:2017-02-12
Issue:1
Volume:31
Page:
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ISSN:2374-3468
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Container-title:Proceedings of the AAAI Conference on Artificial Intelligence
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language:
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Short-container-title:AAAI
Author:
Milli Smitha,Lieder Falk,Griffiths Thomas
Abstract
While optimal metareasoning is notoriously intractable, humans are nonetheless able to adaptively allocate their computational resources. A possible approximation that humans may use to do this is to only metareason over a finite set of cognitive systems that perform variable amounts of computation. The highly influential "dual-process" accounts of human cognition, which postulate the coexistence of a slow accurate system with a fast error-prone system, can be seen as a special case of this approximation. This raises two questions: how many cognitive systems should a bounded optimal agent be equipped with and what characteristics should those systems have? We investigate these questions in two settings: a one-shot decision between two alternatives, and planning under uncertainty in a Markov decision process. We find that the optimal number of systems depends on the variability of the environment and the costliness of metareasoning. Consistent with dual-process theories, we also find that when having two systems is optimal, then the first system is fast but error-prone and the second system is slow but accurate.
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Cited by
1 articles.
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