Model compilation: An approach to automated model derivation

An approach is introduced to automated model derivation for knowledge based systems. The approach, model compilation, involves procedurally generating the set of domain models used by a knowledge based system. With an implemented example, how this approach can be used to derive models of different precision and abstraction is illustrated, and models are tailored to different tasks, from a given set of base domain models. In particular, two implemented model compilers are described, each of which takes as input a base model that describes the structure and behavior of a simple electromechanical device, the Reaction Wheel Assembly of NASA's Hubble Space Telescope. The compilers transform this relatively general base model into simple task specific models for troubleshooting and redesign, respectively, by applying a sequence of model transformations. Each transformation in this sequence produces an increasingly more specialized model. The compilation approach lessens the burden of updating and maintaining consistency among models by enabling their automatic regeneration

Developing a Computational Framework for a Construction Scheduling Decision Support Web Based Expert System

Decision-making is one of the basic cognitive processes of human behaviors by which a preferred option or a course of action is chosen from among a set of alternatives based on certain criteria. Decision-making is the thought process of selecting a logical choice from the available options. When trying to make a good decision, all the positives and negatives of each option should be evaluated. This decision-making process is particularly challenging during the preparation of a construction schedule, where it is difficult for a human to analyze all possible outcomes of each and every situation because, construction of a project is performed in a real time environment with real time events which are subject to change at any time. The development of a construction schedule requires knowledge of the construction process that takes place to complete a project. Most of this knowledge is acquired through years of work/practical experiences. Currently, working professionals and/or students develop construction schedules without the assistance of a decision support system (that provides work/practical experiences captured in previous jobs or by other people). Therefore, a scheduling decision support expert system will help in decision-making by expediting and automating the situation analysis to discover the best possible solution. However, the algorithm/framework needed to develop such a decision support expert system does not exist so far. Thus, the focus of my research is to develop a computational framework for a web-based expert system that helps the decision-making process during the preparation of a construction schedule. My research to develop a new computational framework for construction scheduling follows an action research methodology. The main foundation components for my research are scheduling techniques (such as: Job Shop Problem), path-finding techniques (such as: travelling salesman problem), and rule-based languages (such as JESS). My computational framework is developed by combining these theories. The main contribution of my dissertation to computational science is the new scheduling framework, which consists of a combination of scheduling algorithms that is tested with construction scenarios. This framework could be useful in more areas where automatic job and/or task scheduling is necessary