In the conventional deep learning, the topology of the networks is determined by learning the network parameters. This leads to a lot of useless learning. On the other hand, animals develop innate fundamental ability, called instinct, from their experiences in the environment, and acquire behavioral knowledge with higher-order functions. Such intellectual development can be captured as ”development” rather than ”learning”. In this study, we adopt Weight Agnostic Neural Networks (WANN), a model that captures the topology of networks through their development, as a fundamental technology. We aim at constructing Developmental Artificial Neural Networks (DANNs) that acquire hierarchical relationships of functions, in which higher-order functions expressed from pre-existing lower- order ones. We took up a higher order human behavior, jump forward, and tried to acquire the behavior from two pre-existing lower-order behaviors, walk and jump, applying WANN framework to developing the network including the neural networks of those two behaviors. As a result, we understood that it is necessary to more elaborate our ideas for DANN. |