Computational Processes for General Intelligence The SEOM Model

The ‘brain-mind-intelligence’ structure may be considered analogous to a ‘computer-operating system (OS)-application’ construct. Software development would be far more difficult without the benefit of abstractions like file and memory handles, and graphical user interfaces, implemented in operating systems. The easy access to application-level semantics, provided by the infrastructure of the OS, simplifies application development. Abstractions can play a similar role in producing intelligent systems. The power of abstractions may be determined by their ability to unify the treatment of several types of higher concepts. Unifying various concepts in the domain of intelligence, such as problems, solutions, objects, and emotions, through abstractions, will be of immense value. This paper shows how ‘higher-level perceptions’ could be used to serve this objective and construct an OS-like infrastructure. With the infrastructure in place, ordinary software objects developed and deployed using standard methods, become accessible using the semantics provided by the infrastructure. Now, after brief training exercises, the objects would be available for intelligent use. The focus, therefore, is on the design of the infrastructure that could play a mind-like role in humans, using some ideas from philosophers like Kant, Wittgenstein, Heidegger, and Popper. The design uses a new, non-symbolic, non-connectionist, transmutable method of representation that tackles the so-called ‘frame’ and ‘grounding’ problems of artificial intelligence. The infrastructure enables the conversion of sensory inputs to transmutable representations (amenable to semantic modifications), to flexibly act on software objects developed externally. Furthermore, the infrastructure serves to ground and functionally manage the transmutable, connectionist, and symbolic systems. Thus, the new model plays a critical role in building intelligent systems that overcome major problems in learning and action selection, including brittleness associated with the limited semantics of purely symbolic and connectionist designs. The new model is then evaluated in terms of features, against criteria set forth independently by Newell, Sun, Vernon, and others. In addition, a new measure of intelligence introduced in this paper is used to quantitatively compare the proposed model with some others developed over the years.