Abstract
While much research has been devoted to learning and machine intelligence, the field is still in its infancy. In particular, a technology that will allow for heuristic exploitation of information domain regularities to reduce the time required for knowledge acquisition while concomitantly resulting in an increase in the reliability of the acquired knowledge is still lacking. Unfortunately, contemporary learning mechanisms such as neural network architectures are inherently incapable of such performance. The objective of this paper is to present a new way of looking at learning and machine intelligence which has applicability in many fields such as in robotics, intelligent agents, data fusion, and cooperative sensing. In particular, we propose to construct a new architecture, that is, a transformational architecture for learning, intelligent fusion and transference of knowledge. A System of Systems (SoS) approach is used to realize machine intelligence. Random differences are learned by the system, generalized, and made available for subsequent replay in design transformations. Cross-domain symmetries can play a major role in design generation in particular and in the design of SoSs in general. The fundamental theory of randomization is the science, which underpins the practice. This strategy is employed in the design of the Knowledge Amplification by Structural Expert Randomization or KASER system.