Creativity is one of the most remarkable characteristics of the human mind. It is thus natural that Artificial Intelligence's research groups have been working towards the study and proposal of adequate computational models to creativity. Artificial creative systems are potentially effective in a wide range of artistic, architectural and engineering domains where detailed problem specification is virtually impossible and, therefore, conventional problem solving is unlikely to produce useful solutions. Moreover their study may contribute to the overall understanding of the mechanisms behind human creativity.

The Creative Systems Area of the AI Group/CISUC researches computational models of creativity, looking for sources of inspiration in explanation models for human creativity, originated in the Psychology and Cognitive Science fields, and also in models of natural evolution. We explore symbolic approaches (e.g., CBR) and also non-symbolic approaches (e.g., Genetic Programming). To tune the adopted models, specific research on the evaluation of the creative product is also carried on. In this particular stream, we also explore symbolic (e.g., declarative systems) and non-symbolic solutions (e.g., neural nets).

This is a multidisciplinar area, which requires contributions from a wide set of other areas, mainly from Cognitive Modeling, Case Based Reasoning, Planning, Genetic Algorithms, Neural Nets, Natural Language and Computational Art. The application domains are characterized by requiring highly complex problem solving. Examples are: Music Composition, Visual Composition, Story Generation, and Design.