Search games and rendezvous problems have received growing attention in computer science within the past few years. Rendezvous problems emerge naturally, for instance, to optimize performance and convergence of mobile robots. This gives a new algorithmic point of view to the theory. Furthermore, modern topics such as the spreading of gossip or disease in social networks have lead to new challenging problems in search and rendezvous. Search Theory: A Game Theoretic Perspective introduces the first integrated approach to Search and Rendezvous from the perspectives of biologists, computer scientists and mathematicians. This contributed volume covers a wide range of topics including rendezvous problems and solutions, rendezvous on graphs, search games on biology, mobility in governed social networks, search and security, and more. Most chapters also include case studies or a survey, in addition to a chapter on the future direction of Search and Rendezvous research. This book targets researchers and practitioners working in computer science, mathematics and biology as a reference book. Advanced level students focused on these fields will also find this book valuable as a secondary text book or reference.

On the solution of an optimal search problem with an exponential detection function. Covers one- and two-sided detection problems by furnishing continuous and discret time strategies; examines two-sided search strategies with solutions in hide and seek games in many discrete and continuous bounded

Online Searching puts aspiring librarians working in all types of institutions on the fast track to becoming expert searchers, the intermediaries who unite information users with trusted sources that satisfy their information needs.

In recent years, we have ushered in a new age where applications will become smaller, distributed, JavaScript-laden, microservices-infused, and utilize the hardware of the client to operate. A new paradigm has been forced upon us by the large search providers, and because of this, we can now leverage them to help our applications obtain influence where our applications become the voice of authority on the internet and consequently help our organizations reap the benefits of mass adoption. To better understand this, we must first consider the history that has taken us to where we find ourselves. Architectural Framework for Web Development and Micro Distributed Applications helps readers to come to an understanding of how the indexing domain may be leveraged by this new wave of JavaScript applications that have been termed micro distributed applications and by whose creation and implementation will allow the enterprise to reap the benefit of influence by the existing search systems that the masses utilize. It helps to fill in the picture of the evolution that has occurred and will continue to occur in web development whereby the new breed of applications will become JavaScript-laden and highly distributed and whereby the businesses that implement them will stand a chance to win the indexing race and consequently stand to win the attention of the masses. Covering topics such as distributed systems, search engine optimization, and software as a service, this premier reference source is a dynamic resource for web developers, students and educators of higher education, software developers, technical personnel, IT managers, computer scientists, librarians, researchers, and academicians.

The ability of parallel computing to process large data sets and handle time-consuming operations has resulted in unprecedented advances in biological and scientific computing, modeling, and simulations. Exploring these recent developments, the Handbook of Parallel Computing: Models, Algorithms, and Applications provides comprehensive coverage on a

Terrorist groups throughout the world have been studied primarily through the use of social science methods. However, major advances in IT during the past decade have led to significant new ways of studying terrorist groups, making forecasts, learning models of their behaviour, and shaping policies about their behaviour. Handbook of Computational Approaches to Counterterrorism provides the first in-depth look at how advanced mathematics and modern computing technology is shaping the study of terrorist groups. This book includes contributions from world experts in the field, and presents extensive information on terrorism data sets, new ways of building such data sets in real-time using text analytics, introduces the mathematics and computational approaches to understand terror group behaviour, analyzes terror networks, forecasts terror group behaviour, and shapes policies against terrorist groups. Auxiliary information will be posted on the book’s website. This book targets defence analysts, counter terror analysts, computer scientists, mathematicians, political scientists, psychologists, and researchers from the wide variety of fields engaged in counter-terrorism research. Advanced-level students in computer science, mathematics and social sciences will also find this book useful.

New edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x, with seven new chapters that cover RNNs, AI and Big Data, fundamental use cases, chatbots, and more. Key FeaturesCompletely updated and revised to Python 3.xNew chapters for AI on the cloud, recurrent neural networks, deep learning models, and feature selection and engineeringLearn more about deep learning algorithms, machine learning data pipelines, and chatbotsBook Description Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques. What you will learnUnderstand what artificial intelligence, machine learning, and data science areExplore the most common artificial intelligence use casesLearn how to build a machine learning pipelineAssimilate the basics of feature selection and feature engineeringIdentify the differences between supervised and unsupervised learningDiscover the most recent advances and tools offered for AI development in the cloudDevelop automatic speech recognition systems and chatbotsApply AI algorithms to time series dataWho this book is for The intended audience for this book is Python developers who want to build real-world Artificial Intelligence applications. Basic Python programming experience and awareness of machine learning concepts and techniques is mandatory.