A reference model architecture for intelligent systems design

Introduction Apreviously published outline for a theory of intelligence defines intelligence as “the ability to act appropriately in an uncertain environment, where appropriate action is that which increases the probability of success, and success is the achievement of behavioral goals[1].” The intelligent system acts so as to maximize probability of success and minimize probability of failure. Both goals and success criteria are generated in the environment external to the intelligent system. At a minimum, intelligence requires the abilities to sense the environment, make decisions, and control action. Higher levels of intelligence require the abilities to recognize objects and events, store and use knowledge about the world, and to reason about and plan for the future. Advanced forms of intelligence have the abilities to perceive and analyze, to plot and scheme, to choose wisely and plan successfully in a complex, competitive, hostile world. The amount of intelligence is determined by the computational power of the computing engine, the sophistication and eleg ce of algorithms, the amount and quality of information and values, and the efficiency and reli bility of the system architecture. The amount of intelligence can grow through programming, learning, and evolution. Intelligence is the product of natural selection, wherein more successful behavior is passed on to succeeding generations of intelligent systems, and less successful behavior dies out. Natural selection is driven by competition between individuals within a group, and groups within the world. The above theory of intelligence is expressed in terms of a reference model architecture for real-time intelligent control systems based on the RCS (Real-time Control System) [2]. RCS partitions the control problem into four basic elements: behavior generation (or task