Download or read book An Evolutionary Approach to Learning in Robots written by and published by . This book was released on 1994 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary learning methods have been found to be useful in several areas in the development of intelligent robots. In the approach described here, evolutionary algorithms are used to explore alternative robot behaviors within a simulation model as a way of reducing the overall knowledge engineering effort. This paper presents some initial results of applying the SAMUEL genetic learning system to a collision avoidance and navigation task for mobile robots. (AN).
Download or read book Evolutionary Robotics written by Stefano Nolfi and published by MIT Press. This book was released on 2000 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of the basic concepts and methodologies of evolutionary robotics, which views robots as autonomous artificial organisms that develop their own skills in close interaction with the environment and without human intervention.
Download or read book Darwin2K written by Chris Leger and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Darwin2K: An Evolutionary Approach to Automated Design for Robotics is an essential reference tool for researchers, professionals, and students involved in robot design or in evolutionary synthesis, design, and optimization. It is also necessary for users of Darwin2K. Researchers and hobbyists interested in genetic algorithms and artificial life techniques will find the book interesting. The primary purpose of this book is to describe a methodology for using computers to automatically design robots to meet the specific needs of an application. Details of many novel aspects of the methodology are presented, including an evolutionary algorithm for synthesizing and optimizing multiple objective functions, an algorithm for dynamic simulation of arbitrary robots, an extensible software architecture, and a new representation for robots that is appropriate for robot design. The methodology as a whole is significant in terms of its impact on robot design practices, and as a case study in building evolutionary design systems. Individual parts of the systems are also relevant to other areas. For example, the evolutionary algorithm can be used for design and optimization problems other than robotics, and the dynamic simulation algorithm can be used for analysis and simulation of existing robots or as a part of a manual design tool. The book also gives an overview of previous work in automated design of robots, and of evolutionary design in other engineering disciplines.
Download or read book A Directed Learning based Evolutionary Approach for Legged Robot Motion written by Muh Anshar and published by LAP Lambert Academic Publishing. This book was released on 2010-07 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: At any stage of development, any organism is required to go through a learning phase as a way to acquire and enhance certain skills. Similarly, legged robots perform a learning phase to obtain basic skills before implementing them in real specific applications. Walking skills are one of the first capabilities that need to be learned, as this allows the robots to traverse their environment. This work proposes A Directed Evolutionary Algorithm Learning method - which we henceforth refer to as the DEAL method. This method aims to direct the learning process by incorporating the robot learning experience, and hence, to reduce the time period required for the learning process to converge to a framed optimal solution. This will reduce the deployment time of the robot and as a result, the learning process will have a minimal impact on the wear and tear of the robot body and maximise the life of the robot when it is deployed on specific applications.
Download or read book Mobile Robots The Evolutionary Approach written by Nadia Nedjah and published by Springer Science & Business Media. This book was released on 2007-03-08 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: Researchers have obtained robots that display an amazing slew of behaviors and perform a multitude of tasks, including perception of environment, negotiating rough terrain, and pushing boxes. This volume offers a wide spectrum of sample works developed in leading research throughout the world about evolutionary mobile robotics and demonstrates the success of the technique in evolving efficient and capable mobile robots.
Download or read book Evolutionary Robotics written by Lingfeng Wang and published by World Scientific. This book was released on 2006 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: This invaluable book comprehensively describes evolutionary robotics and computational intelligence, and how different computational intelligence techniques are applied to robotic system design. It embraces the most widely used evolutionary approaches with their merits and drawbacks, presents some related experiments for robotic behavior evolution and the results achieved, and shows promising future research directions. Clarity of explanation is emphasized such that a modest knowledge of basic evolutionary computation, digital circuits and engineering design will suffice for a thorough understanding of the material. The book is ideally suited to computer scientists, practitioners and researchers keen on computational intelligence techniques, especially the evolutionary algorithms in autonomous robotics at both the hardware and software levels. Sample Chapter(s). Chapter 1: Artificial Evolution Based Autonomous Robot Navigation (184 KB). Contents: Artificial Evolution Based Autonomous Robot Navigation; Evolvable Hardware in Evolutionary Robotics; FPGA-Based Autonomous Robot Navigation via Intrinsic Evolution; Intelligent Sensor Fusion and Learning for Autonomous Robot Navigation; Task-Oriented Developmental Learning for Humanoid Robots; Bipedal Walking Through Reinforcement Learning; Swing Time Generation for Bipedal Walking Control Using GA Tuned Fuzzy Logic Controller; Bipedal Walking: Stance Ankle Behavior Optimization Using Genetic Algorithm. Readership: Researchers in evolutionary robotics, and graduate and advanced undergraduate students in computational intelligence.
Download or read book Frontiers in Evolutionary Robotics written by and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Topics in Evolutionary Computation written by and published by . This book was released on 2000 with total page 67 pages. Available in PDF, EPUB and Kindle. Book excerpt: This project contributed new principles for the development of intelligent, mobile robots performing complex tasks in unpredictable environments. In the behavior-based approach to robot design, the overall performance of the robot arises through the interaction of multiple, relatively simple, behaviors. The manual design of multiple interacting behaviors is difficult, labor-intensive and error-prone. One way to reduce the effort in the design of behavior-based robots is to develop an evolutionary approach in which the various behaviors, as well as their modes of interaction, evolve over time. Evolution may also provide a basis for the development of strategies for multiple-robot environments, for example, environments in which a robot is expected to adapt its behavior based on the current behavior of other agents or environmental conditions which themselves are changing over time. This project addressed in four complementary areas concerning the effectiveness of evolutionary algorithms for the design of autonomous robots: (1) learning multiple behaviors by asynchronous co-evolution; (2) continuous and embedded learning; (3) comparison with other reinforcement learning methods, and (4) the ability to evolve responses to changing environments. Results in each of these tasks are reported.
Download or read book Evolutionary Swarm Robotics written by Vito Trianni and published by Springer Science & Business Media. This book was released on 2008-05-30 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book the use of ER techniques for the design of self-organising group behaviours, for both simulated and real robots is introduced. The book tries to mediate between two apparently opposed perspectives: engineering and cognitive science. The experiments presented in the book and the results obtained contribute to the assessment of ER not only as a design tool, but also as a methodology for modelling and understanding intelligent adaptive behaviours.
Download or read book Interdisciplinary Approaches to Robot Learning written by John Demiris and published by World Scientific. This book was released on 2000 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection of papers explore the variety of techniques used to equip robots with the capacity to improve their behaviour over time, based upon their incoming experiences. The contributions are interdisciplinary in nature and combine research from the field of robotics, computer science and biology.
Download or read book Handbook of Evolutionary Machine Learning written by Wolfgang Banzhaf and published by Springer Nature. This book was released on 2023-11-01 with total page 764 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, written by leading international researchers of evolutionary approaches to machine learning, explores various ways evolution can address machine learning problems and improve current methods of machine learning. Topics in this book are organized into five parts. The first part introduces some fundamental concepts and overviews of evolutionary approaches to the three different classes of learning employed in machine learning. The second addresses the use of evolutionary computation as a machine learning technique describing methodologic improvements for evolutionary clustering, classification, regression, and ensemble learning. The third part explores the connection between evolution and neural networks, in particular the connection to deep learning, generative and adversarial models as well as the exciting potential of evolution with large language models. The fourth part focuses on the use of evolutionary computation for supporting machine learning methods. This includes methodological developments for evolutionary data preparation, model parametrization, design, and validation. The final part covers several chapters on applications in medicine, robotics, science, finance, and other disciplines. Readers find reviews of application areas and can discover large-scale, real-world applications of evolutionary machine learning to a variety of problem domains. This book will serve as an essential reference for researchers, postgraduate students, practitioners in industry and all those interested in evolutionary approaches to machine learning.
Download or read book Evolutionary Approach to Machine Learning and Deep Neural Networks written by Hitoshi Iba and published by Springer. This book was released on 2018-06-15 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides theoretical and practical knowledge about a methodology for evolutionary algorithm-based search strategy with the integration of several machine learning and deep learning techniques. These include convolutional neural networks, Gröbner bases, relevance vector machines, transfer learning, bagging and boosting methods, clustering techniques (affinity propagation), and belief networks, among others. The development of such tools contributes to better optimizing methodologies. Beginning with the essentials of evolutionary algorithms and covering interdisciplinary research topics, the contents of this book are valuable for different classes of readers: novice, intermediate, and also expert readers from related fields. Following the chapters on introduction and basic methods, Chapter 3 details a new research direction, i.e., neuro-evolution, an evolutionary method for the generation of deep neural networks, and also describes how evolutionary methods are extended in combination with machine learning techniques. Chapter 4 includes novel methods such as particle swarm optimization based on affinity propagation (PSOAP), and transfer learning for differential evolution (TRADE), another machine learning approach for extending differential evolution. The last chapter is dedicated to the state of the art in gene regulatory network (GRN) research as one of the most interesting and active research fields. The author describes an evolving reaction network, which expands the neuro-evolution methodology to produce a type of genetic network suitable for biochemical systems and has succeeded in designing genetic circuits in synthetic biology. The author also presents real-world GRN application to several artificial intelligent tasks, proposing a framework of motion generation by GRNs (MONGERN), which evolves GRNs to operate a real humanoid robot.
Download or read book Deep Learning for Robot Perception and Cognition written by Alexandros Iosifidis and published by Academic Press. This book was released on 2022-02-04 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. Presents deep learning principles and methodologies Explains the principles of applying end-to-end learning in robotics applications Presents how to design and train deep learning models Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more Uses robotic simulation environments for training deep learning models Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis
Download or read book Evolutionary Robotics written by Philip Husbands and published by Lecture Notes in Computer Science. This book was released on 1998-08-12 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book tackles quantum gravity via the so-called background field method and its effective action functional. The author presents an explicitly covariant and effective technique to calculate the de Witt coefficients and to analyze the Schwinger-de Wit asymptotic expansion of the effective action. He also investigates the ultraviolet behaviour of higher-derivative quantum gravity. The book addresses theoretical physicists, graduate students as well as researchers, but should also be of interest to physicists working in mathematical or elementary particle physics.
Download or read book The Horizons of Evolutionary Robotics written by Patricia A. Vargas and published by MIT Press. This book was released on 2014-03-28 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: An authoritative overview of current research in this exciting interdisciplinary field. Evolutionary robotics (ER) aims to apply evolutionary computation techniques to the design of both real and simulated autonomous robots. The Horizons of Evolutionary Robotics offers an authoritative overview of this rapidly developing field, presenting state-of-the-art research by leading scholars. The result is a lively, expansive survey that will be of interest to computer scientists, robotics engineers, neuroscientists, and philosophers. The contributors discuss incorporating principles from neuroscience into ER; dynamical analysis of evolved agents; constructing appropriate evolutionary pathways; spatial cognition; the coevolution of robot brains and bodies; group behavior; the evolution of communication; translating evolved behavior into design principles; the development of an evolutionary robotics–based methodology for shedding light on neural processes; an incremental approach to complex tasks; and the notion of “mindless intelligence”—complex processes from immune systems to social networks—as a way forward for artificial intelligence. Contributors Christos Ampatzis, Randall D. Beer, Josh Bongard, Joachim de Greeff, Ezequiel A. Di Paolo, Marco Dorigo, Dario Floreano, Inman Harvey, Sabine Hauert, Phil Husbands, Laurent Keller, Michail Maniadakis, Orazio Miglino, Sara Mitri, Renan Moioli, Stefano Nolfi, Michael O'Shea, Rainer W. Paine, Andy Philippides, Jordan B. Pollack, Michela Ponticorvo, Yoon-Sik Shim, Jun Tani, Vito Trianni, Elio Tuci, Patricia A. Vargas, Eric D. Vaughan
Download or read book Evolvability Environments Embodiment Emergence in Robotics written by John H. Long and published by Frontiers Media SA. This book was released on 2018-11-08 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: Embodied and evolving systems — biological or robotic — are interacting networks of structure, function, information, and behavior. Understanding these complex systems is the goal of the research presented in this book. We address different questions and hypotheses about four essential topics in complex systems: evolvability, environments, embodiment, and emergence. Using a variety of approaches, we provide different perspectives on an overarching, unifying question: How can embodied and evolutionary robotics illuminate (1) principles underlying biological evolving systems and (2) general analytical frameworks for studying embodied evolving systems? The answer — model biological processes to operate, develop, and evolve situated, embodied robots.
Download or read book Evolutionary Computing written by Terence C. Fogarty and published by Springer Science & Business Media. This book was released on 1994-09-28 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume is based on the Workshop on Evolutionary Computing held in Leeds, U.K. in April 1994 under the sponsorship of the Society for the Study of Artificial Intelligence and Simulation of Behaviour. In addition to the 22 best papers presented at the workshop, there are two invited contributions by Ray Paton and Colin Reever. The volume addresses several aspects of evolutionary computing, particularly genetic algorithms, and its applications, for example in search, robotics, signal processing, machine learning, and scheduling. The papers are organized in sections on theoretical and biological foundations, techniques, classifier systems, and applications.
