Learning to Perceive and Act by Trial and Error
Machine Learning
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Reinforcement learning with hidden states
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Continual learning in reinforcement environments
Continual learning in reinforcement environments
Adaptive Behavior
Intelligence through simulated evolution: forty years of evolutionary programming
Intelligence through simulated evolution: forty years of evolutionary programming
Illustrating evolutionary computation with Mathematica
Illustrating evolutionary computation with Mathematica
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Theoretical Results on Reinforcement Learning with Temporally Abstract Options
ECML '98 Proceedings of the 10th European Conference on Machine Learning
The MAXQ Method for Hierarchical Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Exact and approximate algorithms for partially observable markov decision processes
Exact and approximate algorithms for partially observable markov decision processes
Planning and control in stochastic domains with imperfect information
Planning and control in stochastic domains with imperfect information
Learning finite-state controllers for partially observable environments
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
SOS++: finding smart behaviors using learning and evolution
ICAL 2003 Proceedings of the eighth international conference on Artificial life
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Finite state machines (FSM) have been successfully used to implement the control of an agent to solve particular sequential tasks. Nevertheless, finite state machines must be hand-coded by the engineer, which might be very difficult for complex tasks. Researchers have used evolutionary techniques to evolve finite state machines and find automatic solutions to sequential tasks. Their approach consists on encoding the state-transition table defining a finite state machine in the genome. However, the search space of such approach tends to be innecesarily huge. In this article, we propose an alternative approach for the automatic design of finite state machines using artificial evolution and learning techniques: the SOS-algorithm. We have obtained very impresive results on experimental work solving partially observable problems.