Representational Systems :: Communication Engineering Papers

Representational Systems This paper seeks to define a representational constitution in such a manner as to be sure-footed of implementation in a connectionist, or neural, web. A representational trunk is defined and demonstrated to be possessed of the ability to produce outputs which achieve international minima. The paper concludes by showing that, while a feed-forward neural network is in overt 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 system of logic. Expert Systems are programmed into computing machine systems by recording the step-by-step lucid methodology of experts to minimize the cost or maximize the utility of their decisions. Logical statements, or beliefs, be they foggy or hard, are established as rules. Another branch of AI, Connectionism, attempts to puddle systems, often in artificial neural networks (ANNs), that implement the methodologies of the illogical, inexplicable, or visceral capabilities of distributed systems such as pattern recognition systems. Here, it is not some logical mapping of input to output, but rather a holistic soldiery 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