Multi-agent reinforcement learning: weighting and partitioning
Neural Networks
Extending the Kohonen self-organizing map networks for clustering analysis
Computational Statistics & Data Analysis
Fuzzy Sets and Systems - Special issue: Approximate Reasoning in Words
Applications of the self-organising map to reinforcement learning
Neural Networks - New developments in self-organizing maps
Robotics and Autonomous Systems
Application of reinforcement learning in robot soccer
Engineering Applications of Artificial Intelligence
A Bayesian classifier for learning opponents' preferences in multi-object automated negotiation
Electronic Commerce Research and Applications
Probabilistic Decision Making in Robot Soccer
RoboCup 2007: Robot Soccer World Cup XI
On-the-fly generation of multi-robot team formation strategies based on game conditions
Expert Systems with Applications: An International Journal
Reward-modulated hebbian learning of decision making
Neural Computation
Policy gradient learning for quadruped soccer robots
Robotics and Autonomous Systems
Engineering Applications of Artificial Intelligence
Clustering and visualizing SOM results
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
Obstacle avoidance of redundant manipulators using neural networks based reinforcement learning
Robotics and Computer-Integrated Manufacturing
Learning form experience: a bayesian network based reinforcement learning approach
ICICA'11 Proceedings of the Second international conference on Information Computing and Applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Reinforcement Learning in Strategy Selection for a Coordinated Multirobot System
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Cooperative strategy based on adaptive Q-learning for robot soccer systems
IEEE Transactions on Fuzzy Systems
Ischemia detection with a self-organizing map supplemented by supervised learning
IEEE Transactions on Neural Networks
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The complex confrontation in robot soccer match requires the decision-making system to learn the priori-knowledge given by humans and learn from its own experience. The two learning issues are usually addressed in two phases: off-line learning and on-line learning. Though lots of methods have been developed to address the two issues separately, the construction of a fully autonomous intelligent decision-making system remains challenging because of the difficulty of connecting the two phases. Most existing intelligent decision-making systems focus on only one of the two phases consequently. The model and algorithms of the Bayesian SOM neural network are proposed in this paper, based on which a fully autonomous intelligent decision-making system for robot soccer is built. This model provides a knowledge structure which can be shared by the off-line learning and on-line learning algorithms. By integrating the Bayesian classifier into each neuron, the whole neural network is equivalent to a multi-agent decision-making system. In the on-line learning phase, the Bayesian method is used to update each neuron's beliefs and the whole network's estimation of the state space. In matches with different opponents, this Bayesian SOM intelligent decision-making system showed outstanding learning ability and great adaptivity.