Accounting for Creativity Within a Psychologically Realistic Cognitive Architecture - Computational Creativity Research: Towards Creative Machines - page 157

In relation to AI, a unified computational model (CLARION) is shown
to be capable of capturing creative problem solving in widely differing settings
(e.g., free recall, lexical decision, problem solving, and so on), demonstrating its
computational capacities. Computationally, the model involves different types of
neural networks to simulate, respectively, explicit processing (with localist, feedfor-
ward networks) and implicit processing (with distributed, fully recurrent, attractor
networks). Integrating these components is essential in capturing creative problem
solving. A computational cognitive architecture is an important way of exploring the
advantage of synergistically combining several specialized computational models,
because so far no single computational model can capture human intelligence by
itself. Future work should be devoted to tackling more complex real-world creative
problem solving situations involving additional factors as detailed earlier.
Better, more integrated computational models of creative problem solving that are
psychologically realistic are needed for both AI and cognitive science. In relation
to AI, they may spur corresponding research on computational creativity. They may
influence and/or challenge common perceptions of where the limits of creativity may
lie and where the limits of intelligent machines may ultimately lie. In the process,
psychologically realistic models of creative problem solving may help to push the
boundaries, one step at a time.
Acknowledgments This research was supported by (1) the ARI research grants DASW01-00-K-
0012 and W74V8H-04-K-0002, and (2) the ONR research grants N00014-08-1-0068 and N00014-
13-1-0342.
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