Cognitive Networks: Towards Self-Aware Networks

9.2. Frameworks for Learning and Reasoning

As a starting point for investigating systems that are capable of learning and reasoning, it is helpful to consider general cognitive architectures. More specifically, we are interested in cognitive architectures that are intended for implementation in a computing device. A variety of such architectures exist with varying degrees of complexity. At the higher complexity end of the scale are the so-called universal theories of cognition such as ACT-R ^[], Soar ^[] and ICARUS ^[]. Each of these architectures seeks to capture all of the components of the human mind and the interactions necessary for cognition. Toward the other end of the complexity scale are the OODA and CECA loops ^[]. These two architectures do not attempt to incorporate all of the elements of human thinking but are intended to model the decision-making process to provide a basis for improved command and control in military decision making. A cognitive architecture that is closely related to the OODA loop is the cognition cycle proposed in ^[], which is the basis for many of the architectures used in cognitive radio research.

9.2.1. Linking Cognitive Architectures to Cognitive Network Architectures

While it is tempting to convert the most human-like cognitive architectures to cognitive network architectures, we must not forget that the purpose of the cognitive network is to exchange data between users and applications within the network. Therefore, it may be appropriate to ...