ARTIFICIAL INTELLIGENCE AND RESPONSIVE OPTIMIZATION-second edition

The purpose of this book is to apply the Artificial Intelligence and control systems to different real models.
we have defined a fuzzy utility system, with different financial goals,different levels of risk tolerance and different personal preferences, liquid assets, etc. A fuzzy system (extendible to a neutrosophic system) has been designed for the evaluations
of the financial objectives. We have investigated the notion of fuzzy and neutrosophiness with respect to time management of money.

we have defined a computational model for a simple portfolio insurance strategy using a protective put and computationally derive the investor’s governing utility structures underlying such a strategy under alternative market scenarios. The Arrow-Pratt
measure of risk aversion has been used to determine how the investors react towards risk under the different scenarios.
It is proposed an artificial classification scheme to isolate truly benign tumors from those that initially start off as benign but subsequently show metastases. A nonparametric artificial neural network methodology has been chosen because of the
analytical difficulties associated with extraction of closed-form stochastic-likelihood parameters given the extremely complicated and possibly non-linear behavior of the state variables we have postulated an in-depth analysis of the numerical output and model
findings and compare it to existing methods of tumor growth modeling and malignancy prediction.an alternative methodological approach has been proposed for quantifying utility in terms of expected information content of the decision-maker’s choice set. It is proposed an extension to the concept of utility by incorporating extrinsic utility; which is defined as the utility derived from the element of choice afforded to the decision-maker.
Book Link:
http://fs.gallup.unm.edu//ArtificialIntelligence-Book2.pdf