Learning in embedded systems
Issues in evolutionary robotics
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Integrating reactive, sequential, and learning behavior using dynamical neural networks
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Evolutionary robotics and the radical envelope-of-noise hypothesis
Adaptive Behavior
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
Autonomous Robots
Evolution of Plastic Control Networks
Autonomous Robots
Evolutionary Robotics: A Survey of Applications and Problems
Proceedings of the First European Workshop on Evolutionary Robotics
Artificial Intelligence Review
Exploring the T-Maze: evolving learning-like robot behaviors using CTRNNs
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Behavioural plasticity in autonomous agents: a comparison between two types of controller
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Evolving reinforcement learning-like abilities for robots
ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
Learning from demonstration in robots: Experimental comparison of neural architectures
Robotics and Computer-Integrated Manufacturing
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In 1994, Yamauchi and Beer (1994) attempted to evolve a dynamic neural network as a control system for a simulated agent capable of performing learning behaviour. They tried to evolve an integrated network, i.e. not modularized; this attempt failed. They ended up having to use independent evolution of separate controller modules, arbitrarily partitioned by the researcher. Moreover, they "provided" the agents with hard-wired reinforcement signals.The model we describe in this paper demonstrates that it is possible to evolve an integrated dynamic neural network that successfully controls the behaviour of a khepera robot engaged in a simple learning task. We show that dynamic neural networks, based on leaky-integrator neuron, shaped by evolution, appear to be able to integrate reactive and learned behaviour with an integrated control system which also benefits from its own evolved reinforcement signal.