Paper
4 April 2008 Computation and design of autonomous intelligent systems
Author Affiliations +
Abstract
This paper describes a theory of intelligent systems and its reduction to engineering practice. The theory is based on a broader theory of computation wherein information and control are defined within the subjective frame of a system. At its most primitive level, the theory describes what it computationally means to both ask and answer questions which, like traditional logic, are also Boolean. The logic of questions describes the subjective rules of computation that are objective in the sense that all the described systems operate according to its principles. Therefore, all systems are autonomous by construct. These systems include thermodynamic, communication, and intelligent systems. Although interesting, the important practical consequence is that the engineering framework for intelligent systems can borrow efficient constructs and methodologies from both thermodynamics and information theory. Thermodynamics provides the Carnot cycle which describes intelligence dynamics when operating in the refrigeration mode. It also provides the principle of maximum entropy. Information theory has recently provided the important concept of dual-matching useful for the design of efficient intelligent systems. The reverse engineered model of computation by pyramidal neurons agrees well with biology and offers a simple and powerful exemplar of basic engineering concepts.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Robert L. Fry "Computation and design of autonomous intelligent systems", Proc. SPIE 6961, Intelligent Computing: Theory and Applications VI, 696102 (4 April 2008); https://doi.org/10.1117/12.782286
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Intelligence systems

Neurons

Telecommunications

Computing systems

Thermodynamics

Probability theory

Information theory

Back to Top