Today, autonomous robots are used in a rather limited range of applications such as exploration of inaccessible locations, cleaning floors, mowing lawns etc. However, ongoing hardware improvements (and human fantasy) steadily reveal new robotic applications of significantly higher sophistication. For such applications, the crucial bottleneck in the engineering process tends to shift from physical boundaries to controller generation. As an attempt to automatize this process, Evolutionary Robotics has successfully been used to generate robotic controllers of various types. However, a major challenge of the field remains the evolution of truly complex behavior. Furthermore, automatically created controllers often lack analyzability which makes them useless for safety-critical applications. In this book, a simple controller model based on Finite State Machines is proposed which allows a straightforward analysis of evolved behaviors. To increase the model's evolvability, a procedure is introduced which, by adapting the genotype-phenotype mapping at runtime, efficiently traverses both the behavioral search space as well as (recursively) the search space of genotype-phenotype mappings. Furthermore, a data-driven mathematical framework is proposed which can be used to calculate the expected success of evolution in complex environments.

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.

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.

An authoritative overview of current research in this exciting interdisciplinary field.

What were the circumstances that led to the development of our cognitive abilities from a primitive hominid to an essentially modern human? The answer to this question is of profound importance to understanding our present nature. Since the steep path of our cognitive development is the attribute that most distinguishes humans from other mammals, this is also a quest to determine human origins. This collection of outstanding scientific problems and the revelation of the many ways they can be addressed indicates the scope of the field to be explored and reveals some avenues along which research is advancing. Distinguished scientists and researchers who have advanced the discussion of the mind and brain contribute state-of-the-art presentations of their field of expertise. Chapters offer speculative and provocative views on topics such as body, culture, evolution, feelings, genetics, history, humor, knowledge, language, machines, neuroanatomy, pathology, and perception. This book will appeal to researchers and students in cognitive neuroscience, experimental psychology, cognitive science, and philosophy. - Includes a contribution by Noam Chomsky, one of the most cited authors of our time

Behavior Trees (BTs) provide a way to structure the behavior of an artificial agent such as a robot or a non-player character in a computer game. Traditional design methods, such as finite state machines, are known to produce brittle behaviors when complexity increases, making it very hard to add features without breaking existing functionality. BTs were created to address this very problem, and enables the creation of systems that are both modular and reactive. Behavior Trees in Robotics and AI: An Introduction provides a broad introduction as well as an in-depth exploration of the topic, and is the first comprehensive book on the use of BTs. This book introduces the subject of BTs from simple topics, such as semantics and design principles, to complex topics, such as learning and task planning. For each topic, the authors provide a set of examples, ranging from simple illustrations to realistic complex behaviors, to enable the reader to successfully combine theory with practice. Starting with an introduction to BTs, the book then describes how BTs relate to, and in many cases, generalize earlier switching structures, or control architectures. These ideas are then used as a foundation for a set of efficient and easy to use design principles. The book then presents a set of important extensions and provides a set of tools for formally analyzing these extensions using a state space formulation of BTs. With the new analysis tools, the book then formalizes the descriptions of how BTs generalize earlier approaches and shows how BTs can be automatically generated using planning and learning. The final part of the book provides an extended set of tools to capture the behavior of Stochastic BTs, where the outcomes of actions are described by probabilities. These tools enable the computation of both success probabilities and time to completion. This book targets a broad audience, including both students and professionals interested in modeling complex behaviors for robots, game characters, or other AI agents. Readers can choose at which depth and pace they want to learn the subject, depending on their needs and background.

What happens when we let robots play the game of life?br Darwin's Devices, Long tells the story of these evolving biorobots -- how they came to be, and what they can teach us about the biology of living and extinct species. Evolving biorobots can replicate creatures that disappeared from the earth long ago, showing us in real time what happens in the face of unexpected environmental challenges. Biomechanically correct models of backbones functioning as part of an autonomous robot, for example, can help us understand why the first vertebrates evolved them.But the most impressive feature of these robots, as Long shows, is their ability to illustrate the power of evolution to solve difficult technological challenges autonomously -- without human input regarding what a workable solution might be. Even a simple robot can create complex behavior, often learning or evolving greater intelligence than humans could possibly program. This remarkable idea could forever alter the face of engineering, design, and even warfare. An amazing tour through the workings of a fertile mind, Darwin's Devices will make you rethink everything you thought you knew about evolution, robot intelligence, and life itself.

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

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.

The study of the brain and behavior is illuminated with the discovery of invariances. Experimental brain research uncovers constancies amidst variation, with respect to interventions and transformations prescribed by experimental paradigms. Place cells, mirror neurons, event related potentials and areas differentially active in fMRI, all illustrate the pervasive role of invariances in neural systems in relation to their function.