Artificial intelligence tools

Computer scientists may use this book to gain a clearer picture of how experimental scientists use Artificial Intelligence tools. Chemists, biochemists, physicists, and others in the experimental sciences who have data to analyze or simulations to run will find tools within these pages that may speed up their work or make it more effective. For both groups, the aim of this book is to encourage a broader application of these methods. 

Wu, C. H., Huang, H. McLarty, J. (1999a). Gene family identification network design. International Journal on Artificial Intelligence Tools, spedal Biocomputing issue (in press). 

Shell, B., The artificial intelligence tool box. In Artificial Intelligence Applications in Business (W. Reitman, ed.), p. 113. 1984. 

In addition to the characteristics of the myriad raw materials, processing parameters also have an important role in determining the performance of the compounded product. Given the limitless ingredient and process combinations possible, formulation by trial and error proves to be a laborious and expensive process. Some effort has been made in recent years to use artificial intelligence tools to shorten the development cycle time and to remove human bias from the development process. 

An important long-term goal is to couple the QSPR scheme developed in this book with artificial intelligence tools [11,22], to develop expert systems with combinatorial search and reverse engineering capabilities [23], (See Venkatasubramanian et al [24-26] for an attempt... 

When mathematical models are difficult to create, artificial intelligence tools, such as search and rule-based systems, can be employed to solve manufacturing design problems. 

Ross P. Plan recognition for intelligent tutoring systems. Workshop IFIP-TC3, Frascati, mai 1987, on Artificial Intelligence Tools in Education, Eds. North Holland. 1988. 

Several examples will be given below, classified upon the artificial intelligence tool, neural networks or support vector machines, respectively. Hybrid combinations are also included in these examples. 

The brain s remarkable ability to learn through a process of pattern recognition suggests that, if we wish to develop a software tool to detect patterns in scientific or, indeed, any other kind of data, the structure of the brain could be a productive starting point. This view led to the development of artificial neural networks (ANNs). The several methods that are gathered under the ANN umbrella constitute some of the most widely used applications of Artificial Intelligence in science. Typical areas in which ANNs are of value include ... 

A decade ago, artificial intelligence (AI) was mainly of interest to computer scientists. Few researchers in the physical sciences were familiar with the area fewer still had tried to put its methods to practical use. However, in the past few years, AI has moved into the mainstream as a routine method for assessing data in the experimental sciences it promises to become one of the most important scientific tools for data analysis. 

Until recently, most expert system building took place in the research departments of universities and a few major corporations. The primary emphasis was investigation of artificial intelligence principles, and the application was of secondary importance. The expert systems tools used reflect this interest. They are typically stand-alone AI computer systems, using special hardware and software environments (usually Lispr-based) not commonly fo md in scientific and engineering organizations. 

KARMA is an interactive computer assisted drug design tool that incorporates quantitative structure-activity relationships (QSAR), conformational analysis, and three-dimensional graphics. It represents a novel approach to receptor mapping analysis when the x-ray structure of the receptor site is not known, karma utilizes real time interactive three-dimensional color computer graphics combined with numerical computations and symbolic manipulation techniques from the field of artificial intelligence. 

Computer-aided trend identification offers potential benefits, but is dependent on the quality of the input information. Expert systems and artificial intelligence are tools being tested. When successful, they may give improved insight into identifying common causes and trend analyses. 

Spectral searches using a library of reference spectra can be a useful tool in identification. Search algorithms have improved over the years and now use the concept of artificial intelligence. Several software packages can be used to conduct searches in spectral libraries in which the main peaks of known compounds are encoded. The compounds offering the best matches are retained as potential candidates. Library searches involve three stages ... 

There will be major changes in synthesis procedures and computer-aided design tools in the years ahead and eventually a strong focus on process innovation. Concepts from artificial intelligence will play a major role, as will improved algorithms and better hardware. Thus, process synthesis,... 

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