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Encouraging far-sightedness with automatically generated descriptions of optimal planning strategies: Potentials and Limitations.

, , , и . CogSci, cognitivesciencesociety.org, (2021)

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Modelling Trial-by-Trial Changes in the Mismatch Negativity., , , , и . PLoS Comput. Biol., (2013)The anchoring bias reflects rational use of cognitive resources, , , и . Psychonomic Bulletin & Review, 25 (1): 322--349 (февраля 2018)Rational Use of Cognitive Resources: Levels of Analysis Between the Computational and the Algorithmic., , и . Top. Cogn. Sci., 7 (2): 217-229 (2015)Have I done enough planning or should I plan more?, , и . CoRR, (2022)Learning to select computations., , , , и . CoRR, (2017)Leveraging Machine Learning to Automatically Derive Robust Planning Strategies from Biased Models of the Environment., , , , , , и . CogSci, cognitivesciencesociety.org, (2020)Automatic discovery of interpretable planning strategies., , и . Mach. Learn., 110 (9): 2641-2683 (2021)Optimal To-Do List Gamification for Long Term Planning., , , и . CoRR, (2021)Leveraging AI for Effective To-Do List Gamification., , и . MuC (Workshopband), Gesellschaft für Informatik e.V., (2022)When Does Bounded-Optimal Metareasoning Favor Few Cognitive Systems?, , и . AAAI, стр. 4422-4428. AAAI Press, (2017)