Robustness of robot programs generated by genetic programming

Authors:
Takuya Ito;Hitoshi Iba;Masayuki Kimura
Affiliations:
Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan;Machine Inference Section, Tsukuba, Ibaragi, Japan;Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan
Venue:
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Year:
1996

Citing 6
Cited 5

Behavior-based artificial intelligence

Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Essentials of artificial intelligence

Essentials of artificial intelligence
Genetic programming II: automatic discovery of reusable programs

Genetic programming II: automatic discovery of reusable programs
Evolution of obstacle avoidance behavior: using noise to promote robust solutions

Advances in genetic programming
The automatic generation of plans for a mobile robot via genetic programming with automatically defined functions

Advances in genetic programming
A simulation of adaptive agents in a hostile environment

SAC '95 Proceedings of the 1995 ACM symposium on Applied computing

An Evaluation of EvolutionaryGeneralisation in Genetic Programming

Artificial Intelligence Review
Multi-agent Robot Learning by Means of Genetic Programming: Solving an Escape Problem

ICES '01 Proceedings of the 4th International Conference on Evolvable Systems: From Biology to Hardware
Evolving robust GP solutions for hedge fund stock selection in emerging markets

Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolutionary behavior learning for action-based environment modeling by a mobile robot

Applied Soft Computing
Evolving controllers for high-level applications on a service robot: a case study with exhibition visitor flow control

Genetic Programming and Evolvable Machines

Quantified Score

Hi-index	0.00

Visualization

Abstract

The goal of this research is to study whether robot programs which are generated by means of genetic programming (GP) are robust. The simulated robot task is a box moving problem. In order to test the robustness of generated programs, we conduct two experiments. In the first experiment, the initial conditions of the robot are fluxed. This is to prevent the overgeneralization of the specific situations. In the second one, the robot's sensors and actuators are assumed to be noisy. As a result of experiments, we have observed that the robot behaves robustly on the both experiments. GP has generated programs which evaluate almost effective commands for each sensor state. We suppose that the robot behaves robustly due to the redundancy of the programs, and we discuss how this redundancy is realized in GP framework for our robot programs.