Representational Systems :: Communication Engineering Papers

Representational Systems This paper seeks to define a representational system in such a manner as to be capable of implementation in a connectionist, or neural, network. A representational system is defined and demonstrated to possess the ability to produce outputs which achieve global minima. The paper concludes by showing that, while a feed-forward neural network is incapable of representation, representation may be implemented in a recurrent, or internal feedback, connectionist network. Introduction Representational systems are commonly in the Artificial Intelligence (AI) domain of symbolic logic. Expert Systems are programmed into computer systems by recording the step-by-step logical methodology of experts to minimize the costs or maximize the utility of their decisions. Logical statements, or beliefs, be they fuzzy or hard, are established as "rules". Another branch of AI, Connectionism, attempts to build systems, often in artificial neural networks (ANNs), that implement the methodologies of the illogical, inexplicable, or intuitive capabilities of distributed systems such as pattern recognition systems. Here, it is not some logical mapping of input to output, but rather a holistic host of inputs which indicate micro-features which may or may not synergistically produce a desired output. While connectionist systems are recognized as being capable of distributed, non-representational processing, they may also possess the capability to additionally perform the rule-based logic of representational systems. As will be shown, not all connectionist networks possess the appropriate architecture for this task. Thus, a neural network, depending upon its architecture, may possess the