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
SimTutor: a multimedia intelligent tutoring system for simulation modeling
Proceedings of the 29th conference on Winter simulation
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
On using learning automata to model a student's behavior in a tutorial-like system
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
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
Modeling a teacher in a tutorial-like system using learning automata
Transactions on Computational Collective Intelligence VIII
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The aim of this paper is to present a novel approach to model a knowledge domain for teaching material in a Tutorial-like system. In this approach, the Tutorial-like system is capable of presenting teaching material within a Socratic model of teaching. The corresponding questions are of a multiple choice type, in which the complexity of the material increases in difficulty. This enables the Tutorial-like system to present the teaching material in different chapters, where each chapter represents a level of difficulty that is harder than the previous one. We attempt to achieve the entire learning process using the Learning Automata (LA) paradigm. In order for the Domain model to possess an increased difficulty for the teaching Environment, we propose to correspondingly reduce the range of the penalty probabilities of all actions by incorporating a scaling factor µ. We show that such a scaling renders it more difficult for the Student to infer the correct action within the LA paradigm. To the best of our knowledge, the concept of modeling teaching material with increasing difficulty using a LA paradigm is unique. The main results we have obtained are that increasing the difficulty of the teaching material can affect the learning of Normal and Below-Normal Students by resulting in an increased learning time, but it seems to have no effect on the learning behavior of Fast Students. The proposed representation has been tested for different benchmark Environments, and the results show that the difficulty of the Environments can be increased by decreasing the range of the penalty probabilities. For example, for some Environments, decreasing the range of the penalty probabilities by 50% results in increasing the difficulty of learning for Normal Students by more than 60%.