TY - JOUR
T1 - A question of balance: The benefits of pattern-recognition when solving problems in a complex domain
AU - Lloyd-Kelly, Martyn
AU - Gobet, Fernand
AU - Lane, Peter
N1 - This is the accepted manuscript version of the following article: M. Lloyd-Kelly, F. Gobet, and Peter C. R. Lane, “A Question of Balance The Benefits of Pattern-Recognition when Solving Problems in a Complex Domain”, LNCS Transactions on Computational Collective Intelligence, Vol. XX, 2015.
The final published version is available at: http://www.springer.com/gb/book/9783319275420
© 2015 Springer International Publishing.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-319-27543-7
DO - 10.1007/978-3-319-27543-7
M3 - Article
SN - 2190-9288
VL - XX
SP - 224
EP - 258
JO - LNCS Transactions on Computational Collective Intelligence
JF - LNCS Transactions on Computational Collective Intelligence
ER -