Deeply Curious Computational Design
نویسندگان
چکیده
This paper describes ongoing work into a model of computational design that is based on curiosity. The model uses goal reformulation to pursue specific unexpected stimuli in order to generate new designs that are creative (i.e. novel and valuable). The model combines a number of techniques from AI, including goal reasoning, heuristic search, genetic algorithms and case-based reasoning, with a deep unsupervised neural network model of expectation. The deep learning model is used by the system to develop a representation of the space of possible designs that respects its latent structure, and can be used as the basis for goal-driven curious search. From this hidden-layer representation similarity functions are derived as well as evaluations of the surprisingness of particular combinations of features. In this position paper we summarize our previous contributions and outline our planned future research in the area of deep learning for AI in design.
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تاریخ انتشار 2016