Open Access
Using a Genetic Algorithm to Learn Strategies for Collision Avoidance and Local Navigation.
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TLDR
SAMUEL, a learning system based on genetic algorithms, is used to learn high-performance reactive strategies for navigation and collision avoidance that also achieve real-time performance.Abstract:
: Navigation through obstacles such as mine fields is an important capability for autonomous underwater vehicles. One way to produce robust behavior is to perform projective planning. However, real-time performance is a critical requirement in navigation. What is needed for a truly autonomous vehicle are robust reactive rules that perform well in a wide variety of situations, and that also achieve real-time performance. In this work, SAMUEL, a learning system based on genetic algorithms, is used to learn high-performance reactive strategies for navigation and collision avoidance. (AN)read more
Citations
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Journal ArticleDOI
The responsibility gap: Ascribing responsibility for the actions of learning automata
TL;DR: Autonomous, learning machines, based on neural networks, genetic algorithms and agent architectures, create a new situation, where the manufacturer/operator of the machine is in principle not capable of predicting the future machine behaviour any more, and thus cannot be held morally responsible or liable for it.
Journal ArticleDOI
From implicit skills to explicit knowledge: a bottom-up model of skill learning
TL;DR: This model is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line reactive learning, and adopts a two-level dual-representation framework (Sun, 1995), with a combination of localist and distributed representation.
Journal ArticleDOI
Challenges in evolving controllers for physical robots
Maja J. Matarić,David Cliff +1 more
TL;DR: The feasibility of applying evolutionary methods to automatically generating controllers for physical mobile robots by describing some of the main approaches and discussing the key challenges, unanswered problems, and some promising directions is discussed.
Proceedings Article
Case-Based Initialization of Genetic Algorithms
TL;DR: A case-based method of initializing genetic algorithms that are used to guide search in changing environments by including strategies, which are learned under similar environmental conditions, in the initial population of the genetic algorithm is introduced.
Journal ArticleDOI
Cooperative formation control of autonomous underwater vehicles: An overview
TL;DR: A brief review on various cooperative search and formation control strategies for multiple autonomous underwater vehicles (AUV) based on literature reported till date and stability analysis of the feasible formation is presented.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book
Adaptation in natural and artificial systems
TL;DR: Names of founding work in the area of Adaptation and modiication, which aims to mimic biological optimization, and some (Non-GA) branches of AI.