Refactored Characteristics of Intelligent Computing Systems

We have discussed the following measurable characteristics of intelligent behavior in computing systems: (1) speed and scope of adaptibility to unforeseen situations; (2) rate of effective learning of observations; (3) accurate modeling and prediction of the relevant external environment; (4) speed and clarity of problem identification and formulation; (5) effective association and evaluation of disparate information; (6) identification of more important assumptions and prerequisites; (7) creation and use of symbolic language. In this paper, we isolate some common underlying capabilities for these characteristics, and show how they can all be produced using those capabilities. We describe the architecture of a system that has all of these underlying capabilities, using our Wrapping integration infrastructure to coordinate and organize a large collection of models and other computational resources. In particular, these models include complete models of the system’s resources and processing strategies, and therefore a model of its own behavior, which it can use to affect