Minimalist mobile robotics: a colony-style architecture for an artificial creature
Minimalist mobile robotics: a colony-style architecture for an artificial creature
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms and Simulated Annealing
Genetic Algorithms and Simulated Annealing
A Distributed Model for Mobile Robot Environment-Learning and Navigation
A Distributed Model for Mobile Robot Environment-Learning and Navigation
Hierarchical genetic algorithms operating on populations of computer programs
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Vector-valued function estimation by grammatical evolution for autonomous robot control
Information Sciences: an International Journal
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The goal in automatic programming is to get a computer to perform a task by telling it what needs to be done. rather than by explicitly programming it. This paper considers the task of automatically generating a computer program to enable an autonomous mobile robot to perform the task of moving a box from the middle of an irregular shaped room to the wall. We compare the ability of the recently developed genetic programming paradigm to produce such a program to the reported ability of reinforcement learning techniques. such as Q learning. to produce such a program in the style of the subsumption architecture. The computational requirements of reinforcement learning necessitates considerable human knowledge and intervention. whereas genetic programming comes much closer to achieving the goal of getting the computer to perform the task without explicitly programming it. The solution produced by genetic programming emerges as a result of Darwinian natural selection and genetic crossover (sexual recombination) in a population of computer programs. The process is driven by a fitness measure which communicates the nature of the task to the computer and its learning paradigm.