Absorbing and ergodic discretized two-action learning automata
IEEE Transactions on Systems, Man and Cybernetics
Deterministic Learning Automata Solutions to the Equipartitioning Problem
IEEE Transactions on Computers
Learning automata: an introduction
Learning automata: an introduction
From computer-aided instruction to intelligent tutoring systems
Educational Technology
On the Computational Complexity of Approximating Distributions by Probabilistic Automata
Machine Learning - Computational learning theory
Learning automata: theory and applications
Learning automata: theory and applications
Refinement-based student modeling and automated bug library construction
Journal of Artificial Intelligence in Education
IEEE/ACM Transactions on Networking (TON)
Learning to teach with a reinforcement learning agent
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Continuous Learning Automata Solutions to the Capacity Assignment Problem
IEEE Transactions on Computers
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
Adaptation of Parameters of BP Algorithm Using Learning Automata
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Learning-Automata-Based MAC Protocols for Photonic LANs
ICON '00 Proceedings of the 8th IEEE International Conference on Networks
Designing a learning-automata-based controller for client/server systems: a methodology
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Learning automata and its application to priority assignment in a queueing system with unknown characteristics
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
String taxonomy using learning automata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
Multiple stochastic learning automata for vehicle path control in an automated highway system
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Modeling a teacher in a tutorial-like system using learning automata
Transactions on Computational Collective Intelligence VIII
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This paper presents a new philosophy to model the behavior of a student in a tutorial-like system using learning automata (LAs). Themodel of the student in our system is inferred using a higher level LA, referred to as a meta-LA, which attempts to characterize the learning model of the students (or student simulators), while the latter use the tutorial-like system. The meta-LA, in turn, uses LAs as a learning mechanism to try to determine if the student in question is a fast, normal, or slow learner. The ultimate long-term goal of the exercise is the following: if the tutorial-like system can understand how the student perceives and processes knowledge, it will be able to customize the way by which it communicates the knowledge to the student to attain an optimal teaching strategy. The proposed meta-LA scheme has been tested for numerous environments, including the established benchmarks, and the results obtained are remarkable. Indeed, to the best of our knowledge, this is the first published result that infers the learning model of an LA when it is externally treated as a black box, whose outputs are the only observable quantities. Additionally, our paper represents a new class of multiautomata systems, where the meta-LA synchronously communicates with the students, also modeled using LAs. The meta-LA's environment "observes" the progress of the student LA, and the response of the latter to the meta-LA actions is based on these observations. This paper also discusses the learning system implications of such a meta-LA.