Computational Heuristic Intelligence
Computational Heuristic Intelligence[1](CHI) is a name for describing specialized programming techniques in the field of Computational intelligence (also called Artificial Intelligence, or AI). These techniques have the express goal of avoiding complexity issues, also called NP-hard problems, by using human-like techniques. They are best summarized as the use of exemplar-based methods (Heuristics), rather than rule-based methods(Algorithms). Hence the term is distinct from the more conventional Computational Algorithmic Intelligence, or GOFAI. An example of a CHI technique is the Encoding specificity principle of Tulving and Thompson.[2] In general, CHI principles are problem solving techniques used by people, rather than programmed into machines. It is by drawing attention to this key distinction that the use of this term is justified in a field already replete with confusing neologisms. Note that the legal systems of all modern human societies employ both heuristics (generalisations of cases) from individual trial records as well as legislated statutes (rules) as regulatory guides.
Another recent approach to the avoidance of complexity issues is to employ feedback control rather than feedforward modeling as a problem-solving paradigm. This approach has been called Computational cybernetics, because (a) the term 'computational' is associated with conventional computer programming techniques which represent a strategic, compiled, or feedforward model of the problem, and (b) the term 'cybernetic' is associated with conventional system operation techniques which represent a tactical, interpreted, or feedback model of the problem. Of course, real programs and real problems both contain both feedforward and feedback components. A real example which illustrates this point is that of human cognition, which clearly involves both perceptual ('bottom-up', feedback, sensor-oriented) and conceptual ('top-down', feedforward, motor-oriented) information flows and hierarchies.