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
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
Learning automata based dynamic guard channel algorithms
Computers and Electrical Engineering
Modeling a teacher in a tutorial-like system using learning automata
Transactions on Computational Collective Intelligence VIII
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Almost all of the learning paradigms used in machine learning, learning automata (LA), and learning theory, in general, use the philosophy of a Student (learning mechanism) attempting to learn from a teacher. This paradigm has been generalized in a myriad of ways, including the scenario when there are multiple teachers or a hierarchy of mechanisms that collectively achieve the learning. In this paper, we consider a departure from this paradigm by allowing the Student to be a member of a classroom of Students, where, for the most part, we permit each member of the classroom not only to learn from the teacher(s) but also to "extract" information from any of his fellow Students. This paper deals with issues concerning the modeling, decision-making process, and testing of such a scenario within the LA context. The main result that we show is that a weak learner can actually benefit from this capability of utilizing the information that he gets from a superior colleague--if this information transfer is done appropriately. As far as we know, the whole concept of Students learning from both a teacher and from a classroom of Students is novel and unreported in the literature. The proposed Student-classroom interaction has been tested for numerous strategies and for different environments, including the established benchmarks, and the results show that Students can improve their learning by interacting with each other. For example, for some interaction strategies, a weak Student can improve his learning by up to 73% when interacting with a classroom of Students, which includes Students of various capabilities. In these interactions, the Student does not have a priori knowledge of the identity or characteristics of the Students who offer their assistance.