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

Martyn Lloyd-Kelly, Fernand Gobet, Peter Lane

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
28 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)224-258
Number of pages35
JournalLNCS Transactions on Computational Collective Intelligence
VolumeXX
DOIs
Publication statusPublished - 2015

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