Artificial intelligence and tutoring systems: computational and cognitive approaches to the communication of knowledge
Genetic programming (videotape): the movie
Genetic programming (videotape): the movie
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
An efficient mapping of fuzzy ART onto a neural architecture
Neural Networks
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
An Efficient Inductive Learning Method for Object-Oriented Database Using Attribute Entropy
IEEE Transactions on Knowledge and Data Engineering
Feature Weighting in k-Means Clustering
Machine Learning
Q-Learning with Adaptive State Segmentation (QLASS)
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
SOS++: finding smart behaviors using learning and evolution
ICAL 2003 Proceedings of the eighth international conference on Artificial life
Learning when and how to coordinate
Web Intelligence and Agent Systems
Self-Organizing Cognitive Agents and Reinforcement Learning in Multi-Agent Environment
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
A Student Agent in a CAI System
IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Hybrid negotiation for resource coordination in multiagent systems
Web Intelligence and Agent Systems
Information needs in agent teamwork
Web Intelligence and Agent Systems
Fuzzy Q-Learning with the modified fuzzy ART neural network
Web Intelligence and Agent Systems
Modified ART 2A growing network capable of generating a fixed number of nodes
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
We propose a new method SSED (State Segmentation based on Euclidean Distance) to categorize continuous numeric percepts for Q-learning, where percept vectors are classified into categories and Q-learning uses categories as states to acquire rules for agent behavior. In SSED, categories are represented by hyper-spheres. A percept vector is classified into a category that covers the vector and is the nearest to it. For efficient reinforcement learning, category merging is provided with SSED, where the number of parameters to control category merging in SSED is fewer than that in fuzzy ART with category merging. In addition, match tracking is incorporated into SSED in order to specialize a category. SSED is combined with Q-learning and it is compared with some state segmentation methods. Experimental results show that Q-learning with SSED learns good rules for agent behavior more efficiently than other methods.