Intelligent Behavior In Animals And Robots

This exciting study explores the novel insight, based on well-established ethological principles, that animals, humans, and autonomous robots can all be analyzed as multi-task autonomous control systems.

August 8-12, 1994, Brighton, England From Animals to Animats 3 brings together research intended to advance the fron tier of an exciting new approach to understanding intelligence. The contributors represent a broad range of interests from artificial intelligence and robotics to ethology and the neurosciences. Unifying these approaches is the notion of "animat" -- an artificial animal, either simulated by a computer or embodied in a robot, which must survive and adapt in progressively more challenging environments. The 58 contributions focus particularly on well-defined models, computer simulations, and built robots in order to help characterize and compare various principles and architectures capable of inducing adaptive behavior in real or artificial animals. Topics include: - Individual and collective behavior. - Neural correlates of behavior. - Perception and motor control. - Motivation and emotion. - Action selection and behavioral sequences. - Ontogeny, learning, and evolution. - Internal world models and cognitive processes. - Applied adaptive behavior. - Autonomous robots. - Heirarchical and parallel organizations. - Emergent structures and behaviors. - Problem solving and planning. - Goal-directed behavior. - Neural networks and evolutionary computation. - Characterization of environments. A Bradford Book

With the science of robotics undergoing a major transformation just now, Springer’s new, authoritative handbook on the subject couldn’t have come at a better time. Having broken free from its origins in industry, robotics has been rapidly expanding into the challenging terrain of unstructured environments. Unlike other handbooks that focus on industrial applications, the Springer Handbook of Robotics incorporates these new developments. Just like all Springer Handbooks, it is utterly comprehensive, edited by internationally renowned experts, and replete with contributions from leading researchers from around the world. The handbook is an ideal resource for robotics experts but also for people new to this expanding field.

The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. By the mid-1980s researchers from artificial intelligence, computer science, brain and cognitive science, and psychology realized that the idea of computers as intelligent machines was inappropriate. The brain does not run "programs"; it does something entirely different. But what? Evolutionary theory says that the brain has evolved not to do mathematical proofs but to control our behavior, to ensure our survival. Researchers now agree that intelligence always manifests itself in behavior—thus it is behavior that we must understand. An exciting new field has grown around the study of behavior-based intelligence, also known as embodied cognitive science, "new AI," and "behavior-based AI." This book provides a systematic introduction to this new way of thinking. After discussing concepts and approaches such as subsumption architecture, Braitenberg vehicles, evolutionary robotics, artificial life, self-organization, and learning, the authors derive a set of principles and a coherent framework for the study of naturally and artificially intelligent systems, or autonomous agents. This framework is based on a synthetic methodology whose goal is understanding by designing and building. The book includes all the background material required to understand the principles underlying intelligence, as well as enough detailed information on intelligent robotics and simulated agents so readers can begin experiments and projects on their own. The reader is guided through a series of case studies that illustrate the design principles of embodied cognitive science.

The Animals to Animats Conference brings together researchers from ethology,psychology, ecology, artificial intelligence, artificial life, robotics, engineering, and relatedfields to further understanding of the behaviors and underlying mechanisms that allow natural andsynthetic agents (animats) to adapt and survive in uncertain environments. The work presentedfocuses on well-defined models--robotic, computer-simulation, and mathematical--that help tocharacterize and compare various organizational principles or architectures underlying adaptivebehavior in both natural animals and animats.

Robots in groups or colonies can exhibit an enormous variety and richness of behaviors which cannot be observed with singly autonomous systems. Of course, this is analogous to the amazing variety of group animal behaviors which can be observed in nature. In recent years more and more investigators have started to study these behaviors. The studies range from classifications and taxonomies of behaviors, to development of architectures which cause such group activities as flocking or swarming, and from emphasis on the role of intelligent agents in such groups to studies of learning and obstacle avoidance. There used to be a time when many robotics researchers would question those who were interested in working with teams of robots: `Why are you worried about robotic teams when it's hard enough to just get one to work?'. This issue responds to that question. Robot Colonies provides a new approach to task problem-solving that is similar in many ways to distributed computing. Multiagent robotic teams offer the possibility of spatially distributed parallel and concurrent perception and action. A paradigm shift results when using multiple robots, providing a different perspective on how to carry out complex tasks. New issues such as interagent communications, spatial task distribution, heterogeneous or homogeneous societies, and interference management are now central to achieving coordinated and productive activity within a colony. Fortunately mobile robot hardware has evolved sufficiently in terms of both cost and robustness to enable these issues to be studied on actual robots and not merely in simulation. Robot Colonies presents a sampling of the research in this field. While capturing a reasonable representation of the most important work within this area, its objective is not to be a comprehensive survey, but rather to stimulate new research by exposing readers to the principles of robot group behaviors, architectures and theories. Robot Colonies is an edited volume of peer-reviewed original research comprising eight invited contributions by leading researchers. This research work has also been published as a special issue of Autonomous Robots (Volume 4, Number 1).

foreword by Lashon Booker To program an autonomous robot to act reliably in a dynamic environment is a complex task. The dynamics of the environment are unpredictable, and the robots' sensors provide noisy input. A learning autonomous robot, one that can acquire knowledge through interaction with its environment and then adapt its behavior, greatly simplifies the designer's work. A learning robot need not be given all of the details of its environment, and its sensors and actuators need not be finely tuned. Robot Shaping is about designing and building learning autonomous robots. The term "shaping" comes from experimental psychology, where it describes the incremental training of animals. The authors propose a new engineering discipline, "behavior engineering," to provide the methodologies and tools for creating autonomous robots. Their techniques are based on classifier systems, a reinforcement learning architecture originated by John Holland, to which they have added several new ideas, such as "mutespec," classifier system "energy,"and dynamic population size. In the book they present Behavior Analysis and Training (BAT) as an example of a behavior engineering methodology.