Creativity and artificial intelligence

The artificial intelligence concept as it is accepted today in the computational sciences was born in 1956, in a summer conference in Dartmouth College, in New Hampshire, United States. It’s base, however, is much older, including disciplines like Philosophy, especially the Logic, Mathematics and Psychology.

The central objective of artificial intelligence is simultaneously theoretical – the creation of theories and models that explains cognitive capacity – and practical – the implementation of computational systems based in those models. For that, cognitive models are developed, as the model of creativity presented in this paper; and computational tools (softwares) that allow the developed cognitive models to be experienced in the proposed implementations are build.

An artificial intelligence implementation that intends to simulate the human creativity should take into account the aspects proposed in the presented model, as well to consider the effects of learning of new situations.

For that, the system should provide conditions for:

    • Express valid emotional answers according to the internal and external pressures on the system;
    • Feed data to the user, emulating the cognitive universe of the system;
    • Build algorithmic and representation systems capable of interrelate concepts;
    • Use categorizations and very defined, however flexible, indexes of abilities and competences.

An interesting problem to solve (complex, according to the criterion adopted in this article) is the subject of the amount and types of competencies and abilities that should be introduced in the system, as well as the subject of how to combine them according to the experiences. In other words, how to solve the problem of integration among Domain, Problems Fields, Cognitive Universe and Emotions, as well as the ways that each one should interact with the problem to form the space of initial research.

Another pertinent subject is which cognitive strategies are adopted by the user to build the reasoning – in other words, which are the parameters used to the initial and to the subsequent processing. A person that thinks preponderantly using visual thought (artists, geographers, advertising people etc.) possesses different mental resources than somebody that thinks using mostly natural language, for instance. So, it can be deduced that the more cognitive strategies the individual possess, the more easiness he/she will have in the transition among the fields, therefore the greater will be the creative potential. A system of artificial intelligence that intends "to teach" the creativity should promote the facilitation of transitional process among the fields and domains, through the placement of "obstacles" in the cognitive strategies commonly used by each user. The insistence in the usual strategies will deliver inadequate or unsatisfactory answers and, finally, the system "will motivate" the user to appeal to new fields in search of new solutions.


It is objective of this article that these considerations can inspire new research lines in the sense of enlarging the concepts and the uses of the artificial intelligence technologies in the study and teaching of creativity. For that, some interesting issues were pointed out, resulting in an outline for the construction of a model of creativity. The implementation of this model for a artificial intelligence system seeking the teaching of creativity should consider technical subjects (which would the fields pre-defined? how should the interaction with the user be? which emotional answers will be considered valid for the system? in what forms the user will participate in the formulation of those emotional responses? how to implement such system?) and it indicates some studies to be developed (cognitive strategies can be drawn from the concepts of mental abilities proposed by Gardner). In these aspects, the present study is important for the formation of new research lines and for the development of new Architecture of Creativity, reconciling the cognitive and empiric aspects.


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