Presenting recent results and ongoing research in Artificial Intelligence, this book has a strong emphasis on fundamental questions in several key areas: programming languages, automated reasoning, natural language processing and computer vision.AI is at the source of major programming language design efforts. Different approaches are described, with some of their most significant results: languages combining logic and functional styles, logic and parallel, functional and parallel, logic with constraints.A central problem in AI is automated reasoning, and formal logic is, historically, at the root of research in this domain. This book presents results in automatic deduction, non-monotonic reasoning, non-standard logic, machine learning, and common-sense reasoning. Proposals for knowledge representation and knowledge engineering are described and the neural net challenger to classical symbolic AI is also defended.Finally, AI systems must be able to interact with their environment in a natural and autonomous way. Natural language processing is an important part of this. Various results are presented in discourse planning, natural language parsing, understanding and generation. The autonomy of a machine for perception of its physical environment is also an AI problem and some research in image processing and computer vision is described.

From its early beginnings in the fifties and sixties the field of neural networks has been steadily growing. The first wave was driven by a handful of pioneers who first discovered analogies between machines and biological systems in communication, control and computing. Technological constraints held back research considerably, but gradually computers have become less expensive and more accessible and software tools inceasingly more powerful. Mathematical techniques, developed by computer-aware people, have steadily accumulated and the second wave has begun. Researchers from such diverse areas as psychology, mathematics, physics, neuroscience and engineering now work together in the neural networking field.

A calculation of the social returns to innovation /Benjamin F. Jones and Lawrence H. Summers --Innovation and human capital policy /John Van Reenen --Immigration policy levers for US innovation and start-ups /Sari Pekkala Kerr and William R. Kerr --Scientific grant funding /Pierre Azoulay and Danielle Li --Tax policy for innovation /Bronwyn H. Hall --Taxation and innovation: what do we know? /Ufuk Akcigit and Stefanie Stantcheva --Government incentives for entrepreneurship /Josh Lerner.

The Compound-Nuclear Reaction and Related Topics (CNR*) international workshop series was initiated in 2007 with a meeting near Yosemite National Park. It has since been held in Bordeaux (2009), Prague (2011), Sao Paulo (2013), Tokyo (2015), and Berkeley, California (2018). The workshop series brings together experts in nuclear theory, experiment, data evaluations, and applications, and fosters interactions among these groups. Topics of interest include: nuclear reaction mechanisms, optical model, direct reactions and the compound nucleus, pre-equilibrium reactions, fusion and fission, cross section measurements (direct and indirect methods), Hauser-Feshbach theory (limits and extensions), compound-nuclear decays, particle and gamma emission, level densities, strength functions, nuclear structure for compound-nuclear reactions, nuclear energy, nuclear astrophysics, and other topics. This peer-reviewed proceedings volume presents papers and poster summaries from the 6th International Workshop on Compound-Nuclear Reactions and Related Topics CNR*18, held on September 24-28, 2018, at Lawrence Berkeley National Lab, Berkeley, CA.

This book honours Partha Dasgupta, and the field he helped establish; environment and development economics. It concerns the relationship between social systems and natural systems. Above all, it concerns the poverty-environment nexus: the complex pathways by which people become or remain poor, and resources become or remain overexploited.

This book extends the theory of revealed preference to fuzzy choice functions, providing applications to multicriteria decision making problems. The main topics of revealed preference theory are treated in the framework of fuzzy choice functions. New topics, such as the degree of dominance and similarity of vague choices, are developed. The results are applied to economic problems where partial information and human subjectivity involve vague choices and vague preferences.

Intended for engineers and researchers interested in the applications of neural networks to signal and image processing, this book is theoretically based with emphasis on application and implementation. Coverage includes neural networks for representation, unsupervised networks for association/classification, neural networks for generalization/restoration, neural net and conventional optimization techniques, and special purpose supercomputers for neural nets.