Artificial Intelligence


Book Description

This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. Artificial Intelligence: Structures and Strategies for Complex Problem Solving is ideal for a one- or two-semester undergraduate course on AI. In this accessible, comprehensive text, George Luger captures the essence of artificial intelligence–solving the complex problems that arise wherever computer technology is applied. Ideal for an undergraduate course in AI, the Sixth Edition presents the fundamental concepts of the discipline first then goes into detail with the practical information necessary to implement the algorithms and strategies discussed. Readers learn how to use a number of different software tools and techniques to address the many challenges faced by today’s computer scientists.




The Democratization of Artificial Intelligence


Book Description

After a long time of neglect, Artificial Intelligence is once again at the center of most of our political, economic, and socio-cultural debates. Recent advances in the field of Artifical Neural Networks have led to a renaissance of dystopian and utopian speculations on an AI-rendered future. Algorithmic technologies are deployed for identifying potential terrorists through vast surveillance networks, for producing sentencing guidelines and recidivism risk profiles in criminal justice systems, for demographic and psychographic targeting of bodies for advertising or propaganda, and more generally for automating the analysis of language, text, and images. Against this background, the aim of this book is to discuss the heterogenous conditions, implications, and effects of modern AI and Internet technologies in terms of their political dimension: What does it mean to critically investigate efforts of net politics in the age of machine learning algorithms?




NASA SP.


Book Description




Foundational Issues in Artificial Intelligence and Cognitive Science


Book Description

The book focuses on a conceptual flaw in contemporary artificial intelligence and cognitive science. Many people have discovered diverse manifestations and facets of this flaw, but the central conceptual impasse is at best only partially perceived. Its consequences, nevertheless, visit themselves asdistortions and failures of multiple research projects - and make impossible the ultimate aspirations of the fields. The impasse concerns a presupposition concerning the nature of representation - that all representation has the nature of encodings: encodingism. Encodings certainly exist, butencodingism is at root logically incoherent; any programmatic research predicted on it is doomed too distortion and ultimate failure. The impasse and its consequences - and steps away from that impasse - are explored in a large number of projects and approaches. These include SOAR, CYC, PDP, situated cognition, subsumption architecture robotics, and the frame problems - a general survey of the current research in AI and Cognitive Science emerges. Interactivism, an alternative model of representation, is proposed and examined.




Simulation and Learning


Book Description

The main idea of this book is that to comprehend the instructional potential of simulation and to design effective simulation-based learning environments, one has to consider both what happens inside the computer and inside the students' minds. The framework adopted to do this is model-centered learning, in which simulation is seen as particularly effective when learning requires a restructuring of the individual mental models of the students, as in conceptual change. Mental models are by themeselves simulations, and thus simulation models can extend our biological capacity to carry out simulative reasoning. For this reason, recent approaches in cognitive science like embodied cognition and the extended mind hypothesis are also considered in the book.. A conceptual model called the “epistemic simulation cycle” is proposed as a blueprint for the comprehension of the cognitive activies involved in simulation-based learning and for instructional design.




Social Science Research


Book Description

This book is designed to introduce doctoral and graduate students to the process of conducting scientific research in the social sciences, business, education, public health, and related disciplines. It is a one-stop, comprehensive, and compact source for foundational concepts in behavioral research, and can serve as a stand-alone text or as a supplement to research readings in any doctoral seminar or research methods class. This book is currently used as a research text at universities on six continents and will shortly be available in nine different languages.




Design News


Book Description




Possible Worlds, Artificial Intelligence, and Narrative Theory


Book Description

In this important contribution to narrative theory, Marie-Laure Ryan applies insights from artificial intelligence and the theory of possible worlds to the study of narrative and fiction. For Ryan, the theory of possible worlds provides a more nuanced way of discussing the commonplace notion of a fictional "world," while artificial intelligence contributes to narratology and the theory of fiction directly via its researches into the congnitive processes of texts and automatic story generation. Although Ryan applies exotic theories to the study of narrative and to fiction, her book maintains a solid basis in literary theory and makes the formal models developed by AI researchers accessible to the student of literature. By combining the philosophical background of possible world theory with models inspired by AI, the book fulfills a pressing need in narratology for new paradigms and an interdisciplinary perspective.




Analyzing Animal Societies


Book Description

Animals lead rich social lives. They care for one another, compete for resources, and mate. Within a society, social relationships may be simple or complex and usually vary considerably, both between different groups of individuals and over time. These social systems are fundamental to biological organization, and animal societies are central to studies of behavioral and evolutionary biology. But how do we study animal societies? How do we take observations of animals fighting, grooming, or forming groups and produce a realistic description or model of their societies? Analyzing AnimalSocieties presents a conceptual framework for analyzing social behavior and demonstrates how to put this framework into practice by collecting suitable data on the interactions and associations of individuals so that relationships can be described, and, from these, models can be derived. In addition to presenting the tools, Hal Whitehead illustrates their applicability using a wide range of real data on a variety of animal species—from bats and chimps to dolphins and birds. The techniques that Whitehead describes will be profitably adopted by scientists working with primates, cetaceans, birds, and ungulates, but the tools can be used to study societies of invertebrates, amphibians, and even humans. Analyzing AnimalSocieties will become a standard reference for those studying vertebrate social behavior and will give to these studies the kind of quality standard already in use in other areas of the life sciences.