Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Tree based discretization for continuous state space reinforcement learning
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Fuzzy Control
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
An Algorithmic Description of ACS2
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Learning classifier systems: a survey
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A Learning Classifier System Approach to Relational Reinforcement Learning
Learning Classifier Systems
Adaptive state space partitioning for reinforcement learning
Engineering Applications of Artificial Intelligence
Fuzzy inference system learning by reinforcement methods
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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This paper describes a simulation approach for modelling decision-making processes under incomplete and imperfect information in Agent-based Computational Economics (ACE). The main idea is to represent decision-making in a model-free framework that can be applied to a larger set of simulation problems, not just the domain modelled. The method translates some basic sociopsychological concepts from the bounded rationality and learning literature into an executable algorithm. In a simple example, the algorithm is applied in the domain of behavioural game theory, illustrating how the algorithm can be used to reproduce observed patterns of human behaviour.