The purpose of this book is to provide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes: - the first volume brings together twenty-three chapters dealing with the foundations of knowledge representation and the formalization of reasoning and learning (Volume 1. Knowledge representation, reasoning and learning) - the second volume offers a view of AI, in fourteen chapters, from the side of the algorithms (Volume 2. AI Algorithms) - the third volume, composed of sixteen chapters, describes the main interfaces and applications of AI (Volume 3. Interfaces and applications of AI). Implementing reasoning or decision making processes requires an appropriate representation of the pieces of information to be exploited. This first volume starts with a historical chapter sketching the slow emergence of building blocks of AI along centuries. Then the volume provides an organized overview of different logical, numerical, or graphical representation formalisms able to handle incomplete information, rules having exceptions, probabilistic and possibilistic uncertainty (and beyond), as well as taxonomies, time, space, preferences, norms, causality, and even trust and emotions among agents. Different types of reasoning, beyond classical deduction, are surveyed including nonmonotonic reasoning, belief revision, updating, information fusion, reasoning based on similarity (case-based, interpolative, or analogical), as well as reasoning about actions, reasoning about ontologies (description logics), argumentation, and negotiation or persuasion between agents. Three chapters deal with decision making, be it multiple criteria, collective, or under uncertainty. Two chapters cover statistical computational learning and reinforcement learning (other machine learning topics are covered in Volume 2). Chapters on diagnosis and supervision, validation and explanation, and knowledge base acquisition complete the volume.

This book is centered around the development of agile, high-performing healthcare institutions that are well integrated into their environment. The aim is to take advantage of artificial intelligence, optimization and simulation methods to provide solutions to prevent, anticipate, monitor and follow public health developments in order to intervene at the right time, using tools and resources that are both appropriate and effective. The focus is on the people involved – the patients, as well as medical, technical and administrative staff – in an effort to provide an efficient healthcare and working environment that meets safety, quality and productivity requirements. Heathcare Systems has been written by healthcare professionals, researchers in science and technology as well as in the social sciences and humanities from various French-speaking countries. It explores the challenges and opportunities presented by digital technology in our practices, organizations and management techniques.

This book is a collection of chapters in the newly developing area of ethics in artificial intelligence. The book comprises chapters written by leading experts in this area which makes it a one of its kind collections. Some key features of the book are its unique combination of chapters on both theoretical and practical aspects of integrating ethics into artificial intelligence. The book touches upon all the important concepts in this area including bias, discrimination, fairness, and interpretability. Integral components can be broadly divided into two segments – the first segment includes empirical identification of biases, discrimination, and the ethical concerns thereof in impact assessment, advertising and personalization, computational social science, and information retrieval. The second segment includes operationalizing the notions of fairness, identifying the importance of fairness in allocation, clustering and time series problems, and applications of fairness in software testing/debugging and in multi stakeholder platforms. This segment ends with a chapter on interpretability of machine learning models which is another very important and emerging topic in this area.

In the evolving environment of bioinformatics, genomics, and computational biology, academic scholars are facing a challenging challenge – keeping informed about the latest research trends and findings. With unprecedented advancements in sequencing technologies, computational algorithms, and machine learning, these fields have become indispensable tools for drug discovery, disease research, genome sequencing, and more. As scholars strive to decode the language of DNA, predict protein structures, and navigate the complexities of biological data analysis, the need for a comprehensive and up-to-date resource becomes paramount. The Research Anthology on Bioinformatics, Genomics, and Computational Biology is a collection of a carefully curated selection of chapters that serves as the solution to the pressing challenge of keeping pace with the dynamic advancements in these critical disciplines. This anthology is designed to address the informational gap by providing scholars with a consolidated and authoritative source that sheds light on critical issues, innovative theories, and transformative developments in the field. It acts as a single reference point, offering insights into conceptual, methodological, technical, and managerial issues while also providing a glimpse into emerging trends and future opportunities.

Provides essential economic tools to think about the impact of Artificial Intelligence on society, both over the short-and long term.

This textbook connects three vibrant areas at the interface between economics and computer science: algorithmic game theory, computational social choice, and fair division. It thus offers an interdisciplinary treatment of collective decision making from an economic and computational perspective. Part I introduces to algorithmic game theory, focusing on both noncooperative and cooperative game theory. Part II introduces to computational social choice, focusing on both preference aggregation (voting) and judgment aggregation. Part III introduces to fair division, focusing on the division of both a single divisible resource ("cake-cutting") and multiple indivisible and unshareable resources ("multiagent resource allocation"). In all these parts, much weight is given to the algorithmic and complexity-theoretic aspects of problems arising in these areas, and the interconnections between the three parts are of central interest.

This book is a collection of selected high-quality research papers presented at International Conference on Paradigms of Communication, Computing and Data Analytics (PCCDA 2023), held at South Asian University, New Delhi, India, during 22–23 April 2023. It discusses cutting-edge research in the areas of advanced computing, communications and data science techniques. The book is a collection of latest research articles in computation algorithm, communication and data sciences, intertwined with each other for efficiency.

Intelligent agents are employed as the central characters in this new introductory text. Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI. Neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks, planning, and language understanding are each revealed through the growing capabilities of these agents. The book provides a refreshing and motivating new synthesis of the field by one of AI's master expositors and leading researchers. Artificial Intelligence: A New Synthesis takes the reader on a complete tour of this intriguing new world of AI. - An evolutionary approach provides a unifying theme - Thorough coverage of important AI ideas, old and new - Frequent use of examples and illustrative diagrams - Extensive coverage of machine learning methods throughout the text - Citations to over 500 references - Comprehensive index

This book constitutes the refereed proceedings of the 13th Pacific Rim Conference on Artificial Intelligence, PRICAI 2014, held in Gold Coast, Queensland, Australia, in December 2014. The 74 full papers and 20 short papers presented in this volume were carefully reviewed and selected from 203 submissions. The topics include inference; reasoning; robotics; social intelligence. AI foundations; applications of AI; agents; Bayesian networks; neural networks; Markov networks; bioinformatics; cognitive systems; constraint satisfaction; data mining and knowledge discovery; decision theory; evolutionary computation; games and interactive entertainment; heuristics; knowledge acquisition and ontology; knowledge representation, machine learning; multimodal interaction; natural language processing; planning and scheduling; probabilistic.

Diagnosis and Analysis of COVID-19 using Artificial Intelligence and Machine Learning-Based Techniques offers new insights and demonstrates how machine learning (ML), artificial intelligence (AI), and (Internet of Things (IoT) can be used to diagnose and fight COVID-19 infection. Sections also discuss the challenges we face in using these technologies. Chapters cover pathogenesis, transmission, diagnosis, and treatment strategies for COVID-19, Artificial Intelligence and Machine Learning, and Blockchain /IoT Blockchain technology, examining how AI can be applied as a tool for detection and containment of the spread of COVID-19, and on the socioeconomic and educational post-pandemic impacts of the disease. This is a multidisciplinary resource for those engaged in researching COVID-19 and how emerging technologies are being used as tools for detection, transmission and treatment strategies. - Describes the molecular basis of pathogenesis, epidemiology, transmission mechanism, diagnostic approaches, and the mutational landscape of SARS-CoV-2 - Provides insights into post COVID-19 symptoms and consequences - Demonstrates how machine learning, AI, and IoT is used to diagnose and fight COVID-19 infection - Examines the use of Blockchain technology/IoT and interpretation and validation of data obtained from artificial intelligence