Learning automata: an introduction
Learning automata: an introduction
Task differentiation in Polistes wasp colonies: a model for self-organizing groups of robots
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Simulation study of multiple intelligent vehicle control using stochastic learning automata
Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Incremental reinforcement learning for designing multi-agent systems
Proceedings of the fifth international conference on Autonomous agents
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Wasp-like Agents for Distributed Factory Coordination
Autonomous Agents and Multi-Agent Systems
A Comparison Study of Self-Adaptation in Evolution Strategies and Real-Coded Genetic Algorithms
Evolutionary Computation
A new co-mutation genetic operator
EC'08 Proceedings of the 9th WSEAS International Conference on Evolutionary Computing
Models for a multi-agent system based on wasp-like behaviour for distributed patients repartition
EC'08 Proceedings of the 9th WSEAS International Conference on Evolutionary Computing
Multiple stochastic learning automata for vehicle path control in an automated highway system
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A new nonlinear reinforcement scheme for stochastic learning automata
ACMOS'10 Proceedings of the 12th WSEAS international conference on Automatic control, modelling & simulation
Optimizing a new nonlinear reinforcement scheme with Breeder genetic algorithm
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
Generic reinforcement schemes and their optimization
ECC'11 Proceedings of the 5th European conference on European computing conference
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A stochastic automaton can perform a finite number of actions in a random environment. When a specific action is performed, the environment responds by producing an environment output that is stochastically related to the action. The aim is to design an automaton, using a reinforcement scheme based on the computational model of wasp behaviour that can determine the best action guided by past actions and environment responses. Using Stochastic Learning Automata techniques, we introduce a decision/control method for intelligent vehicles receiving data from on-board sensors or from the localization system of highway infrastructure.