The Art of Balance Problem-Solving vs. Pattern-Recognition

摘要

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. The aim of this work is to explore the effect on agent performance of altering the balance of these systems in an environment of varying complexity. This is an important question, both in the realm of explanations of expert behaviour and to AI in general. To achieve this, we implement three distinct types of agent, embodying different balances of their problem-solving and pattern-recognition systems, using a novel, hybrid, humanlike cognitive architecture. These agents are then situated in the virtual, stochastic, multi-agent "Tileworld"domain, whose intrinsic and extrinsic environmental complexity can be precisely controlled and widely varied. This domain provides an adequate test-bed to analyse the research question posed. A number of computational simulations are run. Our results indicate that there is a definite performance benefit for agents which use a mixture of problem-solving and pattern-recognition systems, especially in highly complex environments.