Mobile robots range from the Mars Pathfinder mission's teleoperated Sojourner to the cleaning robots in the Paris Metro. This text offers students and other interested readers an introduction to the fundamentals of mobile robotics, spanning the mechanical, motor, sensory, perceptual, and cognitive layers the field comprises. The text focuses on mobility itself, offering an overview of the mechanisms that allow a mobile robot to move through a real world environment to perform its tasks, including locomotion, sensing, localization, and motion planning.
In The Allure of Machinic Life, John Johnston examines new forms of nascent life that emerge through technical interactions within human-constructed environments—"machinic life"—in the sciences of cybernetics, artificial life, and artificial intelligence. With the development of such research initiatives as the evolution of digital organisms, computer immune systems, artificial protocells, evolutionary robotics, and swarm systems, Johnston argues, machinic life has achieved a complexity and autonomy worthy of study in its own right.
Online decision making under uncertainty and time constraints represents one of the most challenging problems for robust intelligent agents. In an increasingly dynamic, interconnected, and real-time world, intelligent systems must adapt dynamically to uncertainties, update existing plans to accommodate new requests and events, and produce high-quality decisions under severe time constraints.
Today, when computing is pervasive and deployed over a range of devices by a multiplicity of users, we need to develop computer software to interact with both the ever-increasing complexity of the technical world and the growing fluidity of social organizations. The Art of Agent-Oriented Modeling presents a new conceptual model for developing software systems that are open, intelligent, and adaptive. It describes an approach for modeling complex systems that consist of people, devices, and software agents in a changing environment (sometimes known as distributed sociotechnical systems).
Learning to perform complex action strategies is an important problem in the fields of artificial intelligence, robotics, and machine learning. Filled with interesting new experimental results, Learning in Embedded Systems explores algorithms that learn efficiently from trial-and error experience with an external world.
New approaches to artificial intelligence spring from the idea that intelligence emerges as much from cells, bodies, and societies as it does from evolution, development, and learning. Traditionally, artificial intelligence has been concerned with reproducing the abilities of human brains; newer approaches take inspiration from a wider range of biological structures that that are capable of autonomous self-organization.
Building a person has been an elusive goal in artificial intelligence. This failure, John Pollock argues, is because the problems involved are essentially philosophical; what is needed for the construction of a person is a physical system that mimics human rationality. Pollock describes an exciting theory of rationality and its partial implementation in OSCAR, a computer system whose descendants will literally be persons.
The Core Language Engine presents the theoretical and engineering advances embodied in one of the most comprehensive natural language processing systems designed to date. Recent research results from different areas of computational linguistics are integrated into a single elegant design with potential for application to tasks ranging from machine translation to information system interfaces.
Constraint logic programming, the notion of computing with partial information, is becoming recognized as a way of dramatically improving on the current generation of programming languages. This collection presents the best of current work on all aspects of constraint logic programming languages, from theory through language implementation.
Logic-based formalizations of argumentation, which assume a set of formulae and then lay out arguments and counterarguments that can be obtained from these formulae, have been refined in recent years in an attempt to capture more closely real-world practical argumentation. In Elements of Argumentation, Philippe Besnard and Anthony Hunter introduce techniques for formalizing deductive argumentation in artificial intelligence, emphasizing emerging formalizations for practical argumentation.