Larry D. Pyeatt

TL;DR: This paper compares the efficiency of two encoding schemes for Artificial Neural Networks optimized by evolutionary algorithms and solves a more difficult problem: balancing two poles when no information about the velocity is provided as input.

TL;DR: This work presents a decision tree based approach to function approximation in reinforcement learning and finds that the decision tree can provide better learning performance than the neural network function approximation and can solve large problems that are infeasible using table lookup.

Abstract: An artificial intelligence system is used with a conglomeration of fluorescence data to provide a method of improving recognition of an unknown from its spectral pattern. Customized neural network systems allow the ultimate organization and resourceful use of assumption-free variables already existing in a total scanning fluorescence database for a much more comprehensive, discrete and accurate differentiation and matching of spectra than is possible with human memory. The invention provides increased speed of fingerprinting analysis, accuracy and reliability together with a decreased learning curve and heightened objectivity for the analysis.

TL;DR: Investigation of Reinforcement learning's application in the delivery of patient-specific, propofol-induced hypnosis in human volunteers suggests that RL may be considered a viable alternative for solving other difficult closed-loop control problems in medicine.

TL;DR: The learning times and generalization capabilities are compared for neural networks developed using both methods and architectures with no hidden units were produced for the single pole and the two pole problem when velocity information is supplied as an input.