The hierarchical accumulation of knowledge in the distributed adaptive control architecture

Abstract

Animals acquire knowledge as they interact with the world. Several authors define this acquisition as a chain of transformations: data is acquired and converted into information that is converted into knowledge. Moreover, theories on cumulative learning suggest that different cognitive layers accumulate this knowledge, building highly complex skills from low complexity ones. The biologically, based Distributed Adaptive Control cognitive architecture (DAC) has been proposed as a cumulative learning system. DAC contains different layers of control: reactive, adaptive and contextual. This hierarchical organization allows for acquisition of knowledge in a bottom-up interaction, i.e. sampled data is transformed into knowledge. DAC has already been used as a framework to investigate fundamental problems encountered in biology. Here we describe the DAC architecture and present some studies focused on its highest cognitive layer where knowledge is constructed and used. We investigate the roles of reactive and contextual control depending on the characteristics and complexity of the tasks. We also show how multi-sensor information could be integrated in order to acquire and use knowledge optimally. Finally, we discuss the possible problems of cumulative learning and the adopted solutions in the context of DAC.