Stochastic sampling in computer graphics
ACM Transactions on Graphics (TOG)
Learning automata: an introduction
Learning automata: an introduction
An overview of representative problems in location research
Management Science
From computer-aided instruction to intelligent tutoring systems
Educational Technology
Learning automata: theory and applications
Learning automata: theory and applications
Automatic Finding of Main Roads in Aerial Images by Using Geometric-Stochastic Models and Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simulation study of multiple intelligent vehicle control using stochastic learning automata
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Continuous Learning Automata Solutions to the Capacity Assignment Problem
IEEE Transactions on Computers
The use of learning algorithms in ATM networks call admission control problem: a methodology
Computer Networks: The International Journal of Computer and Telecommunications Networking
Learning Algorithms Theory and Applications
Learning Algorithms Theory and Applications
Learning Automata and Stochastic Optimization
Learning Automata and Stochastic Optimization
Graph Partitioning Using Learning Automata
IEEE Transactions on Computers
The Use of Reinforcement Learning Algorithms in Traffic Control of High Speed Networks
Advances in Computational Intelligence and Learning: Methods and Applications
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Stochastic properties of the random waypoint mobility model
Wireless Networks
International Journal of Communication Systems
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Learning automata based intelligent tutorial-like systems
Learning automata based intelligent tutorial-like systems
Modeling a student-classroom interaction in a tutorial-like system using learning automata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modeling a student's behavior in a tutorial-like system using learning automata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modeling a domain in a tutorial-like system using learning automata
Acta Cybernetica
Optimizing QoS routing in hierarchical ATM networks using computational intelligence techniques
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Continuous and discretized pursuit learning schemes: variousalgorithms and their comparison
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Guest editorial learning automata: theory, paradigms, and applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Varieties of learning automata: an overview
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generalized pursuit learning schemes: new families of continuous and discretized learning automata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning automata-based bus arbitration for shared-medium ATM switches
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.00 |
The goal of this paper is to present a novel approach to model the behavior of a Teacher in a Tutorial-like system. In this model, the Teacher is capable of presenting teaching material from a Socratic-type Domain model via multiple-choice questions. Since this knowledge is stored in the Domain model in chapters with different levels of complexity, the Teacher is able to present learning material of varying degrees of difficulty to the Students. In our model, we propose that the Teacher will be able to assist the Students to learn the more difficult material. In order to achieve this, he provides them with hints that are relative to the difficulty of the learning material presented. This enables the Students to cope with the process of handling more complex knowledge, and to be able to learn it appropriately. To our knowledge, the findings of this study are novel to the field of intelligent adaptation using Learning Automata (LA). The novelty lies in the fact that the learning system has a strategy by which it can deal with increasingly more complex/difficult Environments (or domains from which the learning as to be achieved). In our approach, the convergence of the Student models (represented by LA) is driven not only by the response of the Environment (Teacher), but also by the hints that are provided by the latter. Our proposed Teacher model has been tested against different benchmark Environments, and the results of these simulations have demonstrated the salient aspects of our model. The main conclusion is that Normal and Below-Normal learners benefited significantly from the hints provided by the Teacher, while the benefits to (brilliant) Fast learners were marginal. This seems to be in-line with our subjective understanding of the behavior of real-life Students.