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This book promotes a meaningful and appropriate dialogue and cross-disciplinary partnerships on Artificial Intelligence (AI) in governance and disaster management. The frequency and the cost of losses and damages due to disasters are rising every year. From wildfires to tsunamis, drought to hurricanes, floods to landslides combined with chemical, nuclear and biological disasters of epidemic proportions has increased human vulnerability and ecosystem sustainability. Life is not as it used to be and governance to manage disasters cannot be a business as usual. The quantum and proportion of responsibilities with the emergency services has increased many times to strain them beyond their human capacities. Its time that the struggling disaster management services get supported and facilitated by new technology of combining Artificial Intelligence (AI) and Machine Learning (ML) with Data Analytics Technologies (DAT)to serve people and government in disaster management. AI and ML have advanced to a state where they could be utilized for many operations in disaster risk reduction. Even though many disasters cannot be prevented and a number of them are blind natural disasters yet through an appropriate application of AI and ML quick predictions, vulnerability identification and classification of relief and rescue operations could be achieved.
A comprehensive, authoritative, and accessible reference for disaster robotics that covers theory, specific deployments, and ground, air, and marine modalities. This book offers the definitive guide to the theory and practice of disaster robotics. It can serve as an introduction for researchers and technologists, a reference for emergency managers, and a textbook in field robotics. Written by a pioneering researcher in the field who has herself participated in fifteen deployments of robots in disaster response and recovery, the book covers theory and practice, the history of the field, and specific missions. After a broad overview of rescue robotics in the context of emergency informatics, the book provides a chronological summary and formal analysis of the thirty-four documented deployments of robots to disasters that include the 2001 collapse of the World Trade Center, Hurricane Katrina, the 2010 Haiti earthquake, the Deepwater Horizon oil spill, the 2011 Japanese earthquake and tsunami, and numerous mining accidents. It then examines disaster robotics in the typical robot modalities of ground, air, and marine, addressing such topics as robot types, missions and tasks, and selection heuristics for each modality. Finally, the book discusses types of fieldwork, providing practical advice on matters that include collecting data and collaborating with emergency professionals. The field of disaster robotics has lacked a comprehensive overview. This book by a leader in the field, offering a unique combination of the theoretical and the practical, fills the gap.
The mobile robot systems described in this book were selected from among the best available implementations by leading universities and research laboratories. These are robots that have left the lab and been tested in natural and unknown environments. They perform many different tasks, from giving tours to collecting trash. Many have distinguished themselves (usually with first- or second-place finishes) at various indoor and outdoor mobile robot competitions. Each case study is self-contained and includes detailed descriptions of important algorithms, including pseudo-code. Thus this volume serves as a recipe book for the design of successful mobile robot applications. Common themes include navigation and mapping, computer vision, and architecture. Contributors Ronald Arkin, Tucker Balch, Michael Brady, Don Brutzman, Arno Bucken, R. James Firby, Erann Gat, Tony Healy, Ian Horswill, Housheng Hu, Sven Koenig, Kurt Konolige David Kortenkamp, Dave Marco, Bob McGhee, Robin Murphy, Karen Myers, Illah Nourbakhsh, Peter Prokopowicz, Bill Schiller, Reid Simmons, Michael Swain, Sebastian Thrun
​This handbook is a comprehensive source of information, analysis and directions in disaster studies. It goes beyond the oft-explored issues of management and science related to the topic and explores policies, governance, law and decision-making combined with the processes of implementation and enforcement, all the while integrating the latest science and technology updates related to the topic, such as artificial intelligence and early warning systems. It brings together studies which relate to sociology, politics and institutional economics, which work under the impact of resource availability, issues of leadership and international laws. Disasters are trans-boundary and disaster studies are trans-disciplinary. It is this aspect which would form the fulcrum of contributions and present a new, refreshing and innovative design for the handbook. The transformatory pedagogy which started with the Hyogo Framework for action 2005-2015 and The Sendai Framework for Disaster Risk Reduction 2015-2030 outlines seven clear targets and four priorities for action to prevent new and reduce existing disaster risks. The four priority areas around which the book would revolve are (i) Understanding disaster risk; (ii) Strengthening disaster risk governance to manage disaster risk; (iii) Investing in disaster reduction for resilience and; (iv) Enhancing disaster preparedness for effective response, and to "Build Back Better" in recovery, rehabilitation and reconstruction.
Six classic science fiction stories and commentary that illustrate and explain key algorithms or principles of artificial intelligence. This book presents six classic science fiction stories and commentary that illustrate and explain key algorithms or principles of artificial intelligence. Even though all the stories were originally published before 1973, they help readers grapple with two questions that stir debate even today: how are intelligent robots programmed? and what are the limits of autonomous robots? The stories—by Isaac Asimov, Vernor Vinge, Brian Aldiss, and Philip K. Dick—cover telepresence, behavior-based robotics, deliberation, testing, human-robot interaction, the “uncanny valley,” natural language understanding, machine learning, and ethics. Each story is preceded by an introductory note, “As You Read the Story,” and followed by a discussion of its implications, “After You Have Read the Story.” Together with the commentary, the stories offer a nontechnical introduction to robotics. The stories can also be considered as a set of—admittedly fanciful—case studies to be read in conjunction with more serious study. Contents “Stranger in Paradise” by Isaac Asimov, 1973 “Runaround” by Isaac Asimov, 1942 “Long Shot” by Vernor Vinge, 1972 “Catch That Rabbit” by Isaac Asimov, 1944 “Super-Toys Last All Summer Long” by Brian Aldiss, 1969 “Second Variety” by Philip K. Dick, 1953
This volume thoroughly explores the perceptions and ethical considerations surrounding urban artificial intelligence (AI). Tan Yigitcanlar delves into the complex public and professional views on AI, offering invaluable insights for policymakers, urban planners, and developers. As the world rapidly advances technologically, the role of AI has become increasingly significant. AI’s transformative power spans various sectors, revolutionising how we operate and innovate in fields such as healthcare, finance, agriculture, and space exploration. Despite its wide‐reaching impact, the integration of AI into urban planning and development remains relatively underexplored. This is surprising given AI’s immense potential to revolutionise urban design, management, and experience. Comprising eight comprehensive and insightful chapters, this book examines AI’s role in urban contexts, including its applications, public perceptions, and ethical implications. The first part of the guidebook delves into varied perceptions of AI within different urban sectors, presenting detailed perception analyses on AI’s role in urban planning, local government services, disaster management, and the construction industry. The second part shifts focus to the ethical implications and responsible implementation of AI in urban settings. It provides frameworks and strategies to ensure AI technologies contribute positively to urban development while mitigating potential risks and ethical concerns. This volume, alongside its companion Urban Artificial Intelligence: A Guidebook for Understanding Concepts and Technologies, offers a holistic view of Urban Artificial Intelligence. Together, these books provide essential insights for urban planners, policymakers, researchers, and anyone interested in AI and urban development, guiding responsible AI integration to foster smarter, more sustainable, and equitable urban environments.
A comprehensive survey of artificial intelligence algorithms and programming organization for robot systems, combining theoretical rigor and practical applications. This textbook offers a comprehensive survey of artificial intelligence (AI) algorithms and programming organization for robot systems. Readers who master the topics covered will be able to design and evaluate an artificially intelligent robot for applications involving sensing, acting, planning, and learning. A background in AI is not required; the book introduces key AI topics from all AI subdisciplines throughout the book and explains how they contribute to autonomous capabilities. This second edition is a major expansion and reorganization of the first edition, reflecting the dramatic advances made in AI over the past fifteen years. An introductory overview provides a framework for thinking about AI for robotics, distinguishing between the fundamentally different design paradigms of automation and autonomy. The book then discusses the reactive functionality of sensing and acting in AI robotics; introduces the deliberative functions most often associated with intelligence and the capability of autonomous initiative; surveys multi-robot systems and (in a new chapter) human-robot interaction; and offers a “metaview” of how to design and evaluate autonomous systems and the ethical considerations in doing so. New material covers locomotion, simultaneous localization and mapping, human-robot interaction, machine learning, and ethics. Each chapter includes exercises, and many chapters provide case studies. Endnotes point to additional reading, highlight advanced topics, and offer robot trivia.
Acute events of natural origin, spanning atmospheric, biological, geophysical, hydrologic, and oceanographic realms, persistently menace societies globally. Approximately 160 million people annually bear the brunt of these disasters, with certain regions facing disproportionate impacts. The lack of predictability intensifies the challenge, creating intercommunal capacity gaps and amplifying the dire consequences. Utilizing AI and Machine Learning for Natural Disaster Management provides instances of ML in predicting earthquakes. By leveraging seismic data, AI systems can analyze magnitude and patterns, providing invaluable insights to forecast earthquake occurrences and aftershocks. Similarly, the book unveils the potential of ML in simulating floods by recording and analyzing rainfall patterns from previous years. The predictive power extends to hurricanes, where data on wind speed, rainfall, temperature, and moisture converge to anticipate future occurrences, potentially saving millions in property damage.