Experimentation

Robotics, Mechatronics, and Artificial Intelligence: Experimental Circuit Blocks for Designers

Accessible to all readers, including students of secondary school and amateur technology enthusiasts, Robotics, Mechatronics, and Artificial Intelligence simplifies the process of finding basic circuits to perform simple tasks, such as how to control a DC or step motor, and provides instruction on creating moving robotic parts, such as an "eye" or an "ear." Though many companies offer kits for project construction, most experimenters want to design and build their own robots and other creatures specific to their needs and goals. With this new book by Newton Braga, hobbyists and experimenters around the world will be able to decide what skills they want to feature in a project and then choose the right "building blocks" to create the ideal results.

In the past few years the technology of robotics, mechatronics, and artificial intelligence has exploded, leaving many people with the desire but not the means to build their own projects. The author's fascination with and expertise in the exciting field of robotics is demonstrated by the range of simple to complex project blocks he provides, which are designed to benefit both novice and experienced robotics enthusiasts. The common components and technology featured in the project blocks are especially beneficial to readers who need practical solutions that can be implemented easily by their own hands, without incorporating expensive, complicated technology.

Accessible to technicians and hobbyists with many levels of experience, and written to provide inexpensive and creative fun with robotics

Appeals to all sorts of technology enthusiasts, including those involved with electronics, computers, home automation, mechanics, and other areas.

Computational Neuroscience: Realistic Modeling for Experimentalists (Methods & New Frontiers in Neuroscience Series)

Designed primarily as an introduction to realistic modeling methods, Computational Neuroscience: Realistic Modeling for Experimentalists focuses on methodological approaches, selecting appropriate methods, and identifying potential pitfalls. The author addresses varying levels of complexity, from molecular interactions within single neurons to the processing of information by neural networks. He avoids theoretical mathematics and provides just enough of the basic math used by experimentalists. What makes this resource unique is the inclusion of a CD-ROM that furnishes interactive modeling examples. It contains tutorials and demos, movies and images, and the simulation scripts necessary to run the full simulation described in the chapter examples. Each chapter covers: the theoretical foundation; parameters needed; appropriate software descriptions; evaluation of the model; future directions expected; examples in text boxes linked to the CD-ROM; and references. The first book to bring you cutting-edge developments in neuronal modeling. It provides an introduction to realistic modeling methods at levels of complexity varying from molecular interactions to neural networks. The book and CD-ROM combine to make Computational Neuroscience: Realistic Modeling for Experimentalists the complete package for understanding modeling techniques.

Robot Cognition and Navigation: An Experiment with Mobile Robots (Cognitive Technologies)

In this book the author employs a cybernetic view of robot cognition and perception as he examines mobile robot simulations, realizations and experiments, and explains the related mathematical models and algorithms. Chapters are dedicated to map building, path planning, navigation using genetic algorithms, and robot programming packages. Then detailed chapters examine the programs required for robot parameter display, gripper control, sonar reading display, teleoperation, autonomous navigation, image capture and 3D perception.

This book will be useful for students and engineers building intelligent robots, and researchers migrating to this field. The required source code is included in the book or available online.

Control of Redundant Robot Manipulators: Theory and Experiments (Lecture Notes in Control and Information Sciences)

This monograph provides a comprehensive and thorough treatment of the problem of controlling a redundant robot manipulator. It presents the latest research from the field with a good balance between theory and practice. All theoretical developments are verified both via simulation and experimental work on an actual prototype redundant robot manipulator. This book is the first text aimed at graduate students and researchers working in the area of redundant manipulators giving a comprehensive coverage of control of redundant robot manipulators from the viewpoint of theory and experimentation.

Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)

Constraint-Based Design Recovery for Software Reengineering: Theory and Experiments (International Series in Software Engineering)

The great challenge of reverse engineering is recovering design information from legacy code: the `concept recovery' problem. This monograph describes up-to-date research dealing with this problem. It discusses a theory of how a constraint-based approach to program plan recognition can efficiently extract design concepts from source code, and it details experiments in concept recovery that support the authors' claims of scalability. Constraint-Based Design Recovery for Software Reengineering: Theory and Experiments presents models and experiments in sufficient detail so that they can be easily replicated.
This book is intended for researchers or software developers concerned with reverse engineering or reengineering legacy systems. However, it may also interest those researchers who are interested in using plan recognition techniques or constraint-based reasoning. The reader is expected to have a reasonable computer science background (i.e., familiarity with the basics of programming and algorithm analysis), but is not required to have a familiarity with the fields of reverse engineering or artificial intelligence (AI).
This book is designed as a reference for advanced undergraduate or graduate seminar courses in software engineering, reverse engineering, or reengineering. It can also serve as a supplementary textbook for software engineering-related courses, such as those on program understanding or design recovery, for AI-related courses, such as those on plan recognition or constraint satisfaction, and for courses that cover both topics, such as those on AI applications to software engineering.

The Experimental Phenomena of Consciousness: A Brief Dictionary

Experimental Phenomena of Consciousness is the definitive collection of consciousness phenomena in which awareness emerges as an experimental variable. With its comprehensive yet succinct entries, arranged alphabetically, this dictionary will be a valuable reference tool for libraries and researchers at all levels in psychology, neuroscience, and philosophy, who are investigating consciousness, cognition, perception, and attention. It will also be an important addition to the reading lists of courses on consciousness and cognition. Most entries include illustrations and a list of references where a more thorough treatment of the topic can be found. The text is supported by a web page that provides dynamic illustrations and other supplemental material. As the first reference book on the topic, Experimental Phenomena of Consciousness will be a valuable tool for undergraduates, graduate students, professional researchers, and anyone who has an interest in the subject of consciousness.

Robot Intelligence ... With Experiments

Computational Learning Theory and Natural Learning Systems, Vol. II: Intersections between Theory and Experiment

As with Volume I, this second volume represents a synthesis of issues in three historically distinct areas of learning research: computational learning theory, neural network research, and symbolic machine learning. While the first volume provided a forum for building a science of computational learning across fields, this volume attempts to define plausible areas of joint research: the contributions are concerned with finding constraints for theory while at the same time interpreting theoretic results in the context of experiments with actual learning systems. Subsequent volumes will focus on areas identified as research opportunities.

Computational learning theory, neural networks, and AI machine learning appear to be disparate fields; in fact they have the same goal: to build a machine or program that can learn from its environment. Accordingly, many of the papers in this volume deal with the problem of learning from examples. In particular, they are intended to encourage discussion between those trying to build learning algorithms (for instance, algorithms addressed by learning theoretic analyses are quite different from those used by neural network or machine-learning researchers) and those trying to analyze them.

The first section provides theoretical explanations for the learning systems addressed, the second section focuses on issues in model selection and inductive bias, the third section presents new learning algorithms, the fourth section explores the dynamics of learning in feedforward neural networks, and the final section focuses on the application of learning algorithms.

A Bradford Book

Experiments in induction