| The aim of this Thesis is to study, design, and implement systems that could tutor other sub-systems using techniques that traditional real-life Teachers use when they teach real-life Students. The research undertaken is a result of merging the fields of Intelligent Tutoring Systems (ITS) and Learning Automata (LA), and leads to a paradigm referred to as Intelligent Tutorial-like systems. In our proposed novel approach, every component incorporates the fundamental principles of LA. Thus, we model the Student (i.e., the learning mechanism) using an LA, and a Classroom of Students, in which each of them is individually represented by a distinct (and possibly different) LA. We also model the Domain and the Teacher using LA paradigms.;Our research also works within a new philosophical perspective. We relax the constraint that "traditional" Tutorial systems have, namely that of assuming that the Teacher is infallible. Rather, we assume that the Teacher is inherently uncertain of the domain knowledge, and is thus of a stochastic nature. However, although he is not absolutely certain about the material being taught, he is also capable of improving his own "teaching skills" even while the operation of the system proceeds. Finally, we also attempt to model a realistic learning framework, where the Students can learn not only from the Teacher, but also from other colleague Students in the Classroom.;Apart from designing the various components of the system, we have also implemented a prototype which exhibits all these properties.;The Thesis demonstrates, within an experimental framework, that our system resolves some related fundamental conceptual questions. It also possesses promising features that can lead to benefits in "traditional" Tutorial systems. We believe that we have taken a few small, but significant and pioneering steps, in this direction. |
