Contents


Preface

In developing nonlinear expert system simulation models, the proper selection of input variables is a challenging problem. Therefore, a false combination of input variables could prevent the simulation model from achieving the optimal solution. The presented methodology in this book is an applicable approach to input variable

Large-scale ES usually have a hierarchical structure, including personnel and various technical devices, which consume various material, financial, and information resources, as well as energy. As a result, they produce new resources (objects), which are delivered to other similar systems. The main features of ES are large dimensionality and high volatility of their structures, equipment, consumed/ produced objects, and, above all, operation logics and dynamics. In this way, ES are dedicated to a new knowledge representation model, providing convergence of

As a theoretical approach, this book develops a primary survey of the knowledge representation model, providing convergence of classical operations research and modern knowledge engineering. This convergence creates new opportunities for complicated problems of formalization and solution by integrating the best features

This book is a review of classic and Bayesian classification and regression tree approaches with an emphasis on Bayesian approaches, as a first comprehensive review of Bayesian classification and regression trees. Bayesian trees have advantages in comparison to classic tree-based approaches. But Bayesian tree approaches investigate different tree structures with different splitting variables, splitting rules, and tree sizes, so these models can explore the tree space more than classic tree

Also presented are three semiautomatic methods, found in an exploratory study through a literature review that reduces the burden for experts. These methods help to minimize the effects of human biases by reducing the parameters that are required to construct complete node probability tables. These methods are highly reliable on the input data elicited from experts and present one of many probability elicitation techniques as an example, which can improve the input data needed by

the semiautomatic methods and reduce the garbage in/garbage out effect.

students from the beginning to the end of a course.

The book presents an online system for a 3D representation of programming students' profiles on software metrics that quantify effort and quality of programming from the analysis of source codes. The advantages of this system are enabling the analysis of where the learning difficulties begin, the monitoring of how a class evolves along a course, and informing assessment criteria through the dynamic composition of rubric representations. The system proposed presents itself as a relevant tool to assist teachers regarding decisions of an evaluative process, assisting

selection in multi-input simulators of expert systems (ES).

classical operations research and modern knowledge engineering.

of mathematical programming or constraint programming.

approaches.
