Technical Note: \cal Q-Learning
Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Combinatonal Optimization by Learning and Simulation of Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Using a Markov network model in a univariate EDA: an empirical cost-benefit analysis
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Estimation of Distribution Algorithms with Kikuchi Approximations
Evolutionary Computation
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
Filter versus wrapper gene selection approaches in DNA microarray domains
Artificial Intelligence in Medicine
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Cognitive agents must be able to decide their actions based on their recognised states. In general, learning mechanisms are equipped for such agents in order to realise intelligent behaviours. In this paper, we propose a new estimation of distribution algorithms (EDAs) which can acquire effective rules for cognitive agents. Basic calculation procedure of the EDAs is that: 1) select better individuals; 2) estimate probabilistic models; 3) sample new individuals. In the proposed method, instead of the use of individuals, input-output records in episodes are directory used for estimating the probabilistic model by conditional random fields. Therefore, estimated probabilistic model can be regarded as policy so that new input-output records are generated by the interaction between the policy and environments. Computer simulations of probabilistic transition problems show the effectiveness of the proposed method.