Artificial intelligence components

Clark, P. and Porter, B., Building Concept Representations from Reusable Components, in Proceedings of the 14th National Conference on Artificial Intelligence, Providence, RI, 1997, 369. 

The artificial intelligence systems to which sensor arrays are coupled supply the closest likeness to the human olfactory system. Some of the recent theories on olfaction require that the human nose has only relatively few types of receptor, each with low specificity. The activation of differing patterns of these receptors supplies the brain with sufficient information for an odour to be described, if not recognized. As a consequence of this belief, the volatile chemical-sensing systems commercially available only contain from 6 to 32 sensors, each having relatively low specificity. Statistical methods such as principal component analysis, canonical discriminant analysis and Euclidian distances are used for mapping or linked to artificial neural nets as an aid to classification of the odour fingerprints . 

Expert systems fall within the broad computer discipline known as artificial intelligence. They have two essential components - a collection of information about their field of expertise, and a structure for applying that information to the problem at hand. The first component is often referred to as the knowledge or rule base of the system and the second as the inference engine. 

Already in the mid-sixties, the model experiments for peptide syntheses by stereoselective four component condensations demonstrated the potential usefulness of artificial intelligence > for the solution of s)mthetic problems. The optimum reaction conditions for the stereoselective synthesis of a given four component condensation product can be found only with some rather detailed knowledge of the very complex reaction mechanism of four component condensations. The latter could be elucidated only with the aid... 

The data analysis usually involves embedded knowledge of the problem while also allowing the user to fine-tune certain parameters. The tools employed are operations research and artificial intelligence-based algorithms, cost calculators, simulation, flow analysis, and other embedded logic procedures. This component is the most complex because there are few off-the-shelf solvers that can deal with the huge variety of problems that companies face. 

The information-processing capacities of machine components (e.g., knowledge, artificial intelligence, processing speed, algorithms, and data)... 

A third approach was the comparison of different computer aids for design, which comprises conventional CAD and computer aided engineering (CAE) components, as well as artificial intelligence (AI) modules (e.g., for the selection of functional solutions in a specific problem domain). 

In the other hand, We may distinguish between two categories [26] the first consists in designing a solver including components from a meta-heuristic into another one, while the second combines meta-heuristics with other techniques t q)ical of fields such as operations research and artificial intelligence. A prominent represent of the first category is the use of trajectory methods into population based techniques or the use of a specific local search method into a more general trajectory method such as iterated local search. 

Computer library searching of spectral databases is routinely available. The database is usually a component part of the spectrometer although the search may be undertaken remotely. Several attempts have been made to develop artificial intelligence systems for direct spectral interpretation, but to date these have met with limited success. Advances in computer control have allowed multiexperiment analysis in which the spectrometer will follow a set of experiments sequentially while automatically adjusting operating parameters as directed by the results of the preceding experiment. Further advances in this area are anticipated. 

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