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        A question of balance: The benefits of pattern-recognition when solving problems in a complex domain

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        Author
        Lloyd-Kelly, Martyn
        Gobet, Fernand
        Lane, Peter
        Attention
        2299/19027
        Abstract
        The dual-process theory of human cognition proposes the existence of two systems for decision-making: a slower, deliberative,problem-solving system and a quicker, reactive, pattern-recognition system. We alter the balance of these systems in a number of computational simulations using three types of agent equipped with a novel, hybrid, human-like cognitive architecture. These agents are situated in the stochastic, multi-agent Tileworld domain, whose complexity can be precisely controlled and widely varied. We explore how agent performance is affected by different balances of problem-solving and pattern-recognition, and conduct a sensitivity analysis upon key pattern-recognition system variables. Results indicate that pattern-recognition improves agent performance by as much as 36.5 % and, if a balance is struck with particular pattern-recognition components to promote pattern-recognition use, performance can be further improved by up to 3.6 %. This research is of interest for studies of expert behaviour in particular, and AI in general.
        Publication date
        2015
        Published in
        LNCS Transactions on Computational Collective Intelligence
        Published version
        https://doi.org/10.1007/978-3-319-27543-7
        License
        http://creativecommons.org/licenses/by/4.0/
        Other links
        http://hdl.handle.net/2299/19027
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