Artificial intelligence techniques

The DENDRAL project initiated in 1964 at Stanford was the prototypical application of artificial intelligence techniques - or what was understood at that time under this name - to chemical problems. Chemical structure generators were developed and information from mass spectra was used to prune the chemical graphs in order to derive the chemical structure associated with a certain mass spectrum. 

Burns, R.S. (1997) The Application of Artificial Intelligence Techniques to Modelling and Control of Surface Ships. In Proc. 11th Ship Control Systems Symposium, Southampton UK, April, 1, 77-83. 

Chen SH, Jakeman AJ, Norton J-P (2008) Artificial Intelligence techniques an introduction to their use for modelling environmental systems. Math Comput Slmul 78 379 00... 

A mathematical device, the NSS, which can be related to Artificial Intelligence techniques, has been defined and applied in order to solve or reformulate some quantum chemical problems. This symbol is related to computer formulae generation. It has been shown that by means of the use of NSS s many applications of such symbols can be found in mathematics as well as in Mathematical Chemistry in particular. 

Advances in computer science continue to serve as the basis for new extensions to software products. In particular, artificial intelligence techniques have begun to mature to the point at which they can play a role in scientific software. In the future, scientific software will incorporate expert systems technology in order to provide a new level of assistance to scientists in applying statistical and graphical techniques to data analysis. 

Wipke, W. T. Ouchi, G. I. Krishnan, S. "SECS An Application of Artificial Intelligence Techniques . Artificial Intelligence... 

Few simple methods exist for estimating pKa for complex dye structures. However, complex artificial intelligence techniques combining the results of fundamental and empirical approaches have been developed which can predict pKa values for dye structures to within the experimental error of laboratory measurements. 

Wipke, W.T., Ouchi, G.I., and Krishnan, S., Simulation and Evaluation of Chemical Synthesis — SECS — Application of Artificial Intelligence Techniques, Artif. IntelL, 11, 173, 1978. 

The ANN is an artificial intelligent technique that has several distinct advantages over rule-based Expert System and Fuzzy Logic. The technique has been shown to be a feasible technique that estimates the material properties for the FGM design. The estimation accuracy is satisfactory. 

We use an Artificial Intelligence technique called the Problem Decomposition Strategy (14, 15) to tackle this problem. We divide the problem of computing a quantity into a number of sub-problems, each involving the computation of a formula with several sub-quantities. When more than one formula is applicable, they are tried one by one. The entire problem space can be represented as an AND/OR tree, and a Depth-first Recursive Search is employed to traverse the tree. The leaf nodes represent quantities whose values are known. The search terminates at the leaf nodes and returns the value to the level above. When a dead-end is reached, the system progressively backtracks to the levels above in an attempt to select smother formula. If the complete search space is exhausted, the system reports that the problem is unsolvable and prompts the user for more information. 

Kidwell, E. 2005. Intelligent Bioinformatics The Application of Artificial Intelligence Techniques to Bioinformatics Problems. New York Wiley. 

Stored in a table where columns are descriptors, and rows are compounds (or conformers), QSAR data sets contain separate columns for the measured target property (Y), attributed to the training set, as well as computed descriptors for (external) reference compounds on which the QSAR model is tested—the test set. Statistical procedures, e.g., multiple linear regression (MLR), projection to latent structures (PLS), or neural networks (NN) [38], are then used to establish a mathematical soft model relating the observed measurement(s) in the Y column(s) with some combination of the properties represented in the subsequent columns. PLS, NN, and AI (artificial intelligence) techniques have been explored by Green and Marshall in the context of 3D-QSAR models [39], and were shown to extract similar information. A problem that may lead to spurious (chance) correlations when using MLR techniques, the colinearity between various descriptors, or cross-correlation, is usually dealt with in PLS [40],... 

In the preceding section, we described two architectural approaches for organizing functions related to shop-floor schedtrUng control. One of the most important of these functions is schedttUng. Another chapter reported on two major approaches to solving these problems mathematical programming and heuristics. In this chapter, we describe a number of AI (artificial intelligence) techniques. 

Artificial intelligence techniques are applied for the identification of substructures of unknown molecules and for the generation of complete candidate structures based on a substructure analysis and verification of possible structures. 

Thermoeconomics uses results from the synthesis, cost analysis, and simulation of thermal systems and provides useful information for the evaluation and optimization of these systems as well as for the application of artificial intelligence techniques to improve the design and operation of such systems. 

---