The LISP tutor: it approaches the effectiveness of a human tutor
BYTE - Lecture notes in computer science Vol. 174
Artificial intelligence and tutoring systems: computational and cognitive approaches to the communication of knowledge
C4.5: programs for machine learning
C4.5: programs for machine learning
Fundamentals of database systems (2nd ed.)
Fundamentals of database systems (2nd ed.)
WIP: the automatic synthesis of multimodal presentations
Intelligent multimedia interfaces
Deductive error diagnosis and inductive error generalization for intelligent tutoring systems
Journal of Artificial Intelligence in Education
Applications of simulated students: an exploration
Journal of Artificial Intelligence in Education
STEPS: a simulated, tutorable physics student
Journal of Artificial Intelligence in Education
Refinement-based student modeling and automated bug library construction
Journal of Artificial Intelligence in Education
WIP/PPP: automatic generation of personalized multimedia presentations
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
Multistrategy Discovery and Detection of Novice Programmer Errors
Machine Learning - Special issue on multistrategy learning
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
User Modeling and User-Adapted Interaction
Experiments with Incremental Concept Formation: UNIMEM
Machine Learning
ELM-ART: An Intelligent Tutoring System on World Wide Web
ITS '96 Proceedings of the Third International Conference on Intelligent Tutoring Systems
A Web-Based Intelligent Tutoring System Using Hybrid Rules as Its Representational Basis
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Advisor: a machine-learning architecture for intelligent tutor construction
Advisor: a machine-learning architecture for intelligent tutor construction
Design and Analysis of Experiments
Design and Analysis of Experiments
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Evaluating the effectiveness of tutorial dialogue instruction in an exploratory learning context
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Reinforcement learning of pedagogical policies in adaptive and intelligent educational systems
Knowledge-Based Systems
Dynamic analysis of multiagent Q-learning with ε-greedy exploration
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
International Journal of Advanced Intelligence Paradigms
PC2PSO: personalized e-course composition based on Particle Swarm Optimization
Applied Intelligence
User Modeling and User-Adapted Interaction
Using neurophysiological data to inform feedback timing: a pilot study
FAC'11 Proceedings of the 6th international conference on Foundations of augmented cognition: directing the future of adaptive systems
Formation conditions of mutual adaptation in human-agent collaborative interaction
Applied Intelligence
Mining bridging rules between conceptual clusters
Applied Intelligence
Ontology-based user profile learning
Applied Intelligence
International Journal of Artificial Intelligence in Education - Special issue on Best of ITS 2010
Inverse reinforcement learning for interactive systems
Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
Bringing context-aware access to the web through spoken interaction
Applied Intelligence
Monte-Carlo tree search for Bayesian reinforcement learning
Applied Intelligence
Learning via human feedback in continuous state and action spaces
Applied Intelligence
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One of the most important issues in Adaptive and Intelligent Educational Systems (AIES) is to define effective pedagogical policies for tutoring students according to their needs. This paper proposes to use Reinforcement Learning (RL) in the pedagogical module of an educational system so that the system learns automatically which is the best pedagogical policy for teaching students. One of the main characteristics of this approach is its ability to improve the pedagogical policy based only on acquired experience with other students with similar learning characteristics. In this paper we study the learning performance of the educational system through three important issues. Firstly, the learning convergence towards accurate pedagogical policies. Secondly, the role of exploration/exploitation strategies in the application of RL to AIES. Finally, a method for reducing the training phase of the AIES.