This paper presents an information theoretic approach to the concept of intelligence in the computational sense. We introduce a probabilistic framework from which computation alintelligence is shown to be an entropy minimizing process at the local level. Using this new scheme, we develop a simple data driven clustering example and discuss its applications.
Many sources claim to have computational theories of intelligence, but for the most part these theories'' merely act to describe certain aspects of intelligence @cite_6 . For example, Meyer in @cite_12 suggests that performance on multiple tasks is dependent on adaptive executive control, but makes no claim on the emergence of such characteristics. Others discuss how data is aggregated. This type of analysis is especially relevant in computer vision and image recognition @cite_9 .
@cite_9 @cite_12 @cite_6
1995756857 1974302391 1577491022
An algorithm is proposed for solving the stereoscopic matching problem. The algorithm consists of five steps: (1) Each image is filtered at different orientations with bar masks of four sizes that increase with eccentricity; the equivalent filters are one or two octaves wide. (2) Zero-crossings in the filtered images, which roughly correspond to edges, are localized. Positions of the ends of lines and edges are also found. (3) For each mask orientation and size, matching takes place between pairs of zero-crossings or terminations of the same sign in the two images, for a range of disparities up to about the width of the mask's central region. (4) Wide masks can control vergence movements, thus causing small masks to come into correspondence. (5) When a correspondence is achieved, it is stored in a dynamic buffer, called the 2 @math -D sketch. It is shown that this proposal provides a theoretical framework for most existing psychophysical and neurophysiological data about stereopsis. Several critical experimental predictions are also made, for instance about the size of Panum's area under various conditions. The results of such experiments would tell us whether, for example, co-operativity is necessary for the matching process. Abstract : Persistent controversies about human multiple task performance suggest that research on it will benefit from increased use of precise computational models. Toward this objective, the present report outlines a comprehensive theoretical framework for understanding and predicting the performance of concurrent perceptual motor and cognitive tasks. The framework involves an Executive Process Interactive Control (EPIC) architecture, which has component modules that process information at perceptual, cognitive, and motor levels. On the basis of EPIC, computational models that use a production system formalism may be constructed to simulate multiple task performance under a variety of conditions. These models account well for reaction time data from representative paradigms such as the psychological refractory period (PRP) procedure. With modest numbers of parameters, good fits between empirical and simulated reaction times support several key conclusions: (1) at a cognitive level, people can apply distinct sets of production rules simultaneously for executing the procedures of multiple tasks; (2) there is no immutable central response selection or decision bottleneck; (3) people's capacity to process information and take action at peripheral perceptual motor levels is limited; (4) to cope with such limits and to satisfy task priorities, flexible scheduling strategies are used; (5) these strategies are mediated by executive cognitive processes that coordinate concurrent tasks adaptively. The initial success of EPIC and models based on it suggest that they may help characterize multiple task performance across many domains, including ones that have substantial practical relevance. Preface. Emergence of a Theory. Knowledge. Perception. Goal Seeking and Planning. A Reference Model Architecture. Behavior Generation. World Modeling, Value Judgment, and Knowledge Representation. Sensory Processing. Engineering Unmanned Ground Vehicles. Future Possibilities. References. Index.