A framework for sensor evolution in a population of Braitenberg vehicle-like agents (poster)
ALIFE Proceedings of the sixth international conference on Artificial life
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Some Effects of Individual Learning on the Evolution of Sensors
ECAL '01 Proceedings of the 6th European Conference on Advances in Artificial Life
Planning and acting in partially observable stochastic domains
Artificial Intelligence
An Information-Theoretic Approach for the Quantification of Relevance
ECAL '01 Proceedings of the 6th European Conference on Advances in Artificial Life
Some Effects of Individual Learning on the Evolution of Sensors
ECAL '01 Proceedings of the 6th European Conference on Advances in Artificial Life
The physical symbol grounding problem
Cognitive Systems Research
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In this paper, we present an abstract model of sensor evolution, where sensor development is only determined by artificial evolution and the adaptation of agent reactions is accomplished by individual learning. With the environment cast into a MDP framework, sensors can be conceived as a map from environmental states to agent observations and Reinforcement Learning algorithms can be utilised. On the basis of a simple gridworld scenario, we present some results of the interaction between individual learning and evolution of sensors.