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AI for Sports (AI for Everything)

It seems that artificial intelligence (AI) is always just five years away, but it never arrives. Recently, however. developments have made the practical utility of game theory a genuine reality. Will sport provide the petri dish in which AI will prove itself? What do domain specialists like managers and coaches want to know that they can’t currently find out, and can AI provide the answer? What competitive advantages might AI provide for recruitment, performance and tactics, health and fitness, pedagogy, broadcasting, eSports, gambling and stadium design in the future? Written by leading experts in both sports management and AI, AI for Sports begins to answer these and many other questions on the future of AI for sports.

AI for School Teachers (AI for Everything)

What is artificial intelligence? Can I realistically use it in my school? What’s best done by human intelligence vs. artificial intelligence, and how do I bring these strengths together? What would it look like for me, and my school, to be AI Ready? AI for School Teachers will help teachers and headteachers understand enough about AI to build a strategy for how it can be used in their school. Examining the needs of schools to ensure they are ready to leverage the power of AI and drawing examples from early years to high school students, this book outlines the educational implications and benefits that AI brings to school education in practical ways. It develops an understanding of what AI is and isn't and how we define and measure what we value and provides a framework which supports a step-by-step approach to developing an AI mindset, focusing on ways to improve educational opportunities for students with evidence-informed interventions.

AI for School Teachers (AI for Everything)

What is artificial intelligence? Can I realistically use it in my school? What’s best done by human intelligence vs. artificial intelligence, and how do I bring these strengths together? What would it look like for me, and my school, to be AI Ready? AI for School Teachers will help teachers and headteachers understand enough about AI to build a strategy for how it can be used in their school. Examining the needs of schools to ensure they are ready to leverage the power of AI and drawing examples from early years to high school students, this book outlines the educational implications and benefits that AI brings to school education in practical ways. It develops an understanding of what AI is and isn't and how we define and measure what we value and provides a framework which supports a step-by-step approach to developing an AI mindset, focusing on ways to improve educational opportunities for students with evidence-informed interventions.

AI for Learning (AI for Everything)

What is artificial intelligence (AI)? How can AI help a learner, a teacher or a system designer? What are the positive impacts of AI on human learning? AI for Learning examines how artificial intelligence can, and should, positively impact human learning, whether it be in formal or informal educational and training contexts. The notion of ‘can’ is bound up with ongoing technological developments. The notion of ‘should’ is bound up with an ethical stance that recognises the complementary capabilities of human and artificial intelligence, as well as the objectives of doing good, not doing harm, increasing justice and maintaining fairness. The book considers the different supporting roles that can help a learner – from AI as a tutor and learning aid to AI as a classroom moderator, among others – and examines both the opportunities and risks associated with each.

AI for Learning (AI for Everything)

What is artificial intelligence (AI)? How can AI help a learner, a teacher or a system designer? What are the positive impacts of AI on human learning? AI for Learning examines how artificial intelligence can, and should, positively impact human learning, whether it be in formal or informal educational and training contexts. The notion of ‘can’ is bound up with ongoing technological developments. The notion of ‘should’ is bound up with an ethical stance that recognises the complementary capabilities of human and artificial intelligence, as well as the objectives of doing good, not doing harm, increasing justice and maintaining fairness. The book considers the different supporting roles that can help a learner – from AI as a tutor and learning aid to AI as a classroom moderator, among others – and examines both the opportunities and risks associated with each.

AI for Diversity (AI for Everything)

Artificial intelligence (AI) is increasingly impacting many aspects of people’s lives across the globe, from relatively mundane technology to more advanced digital systems that can make their own decisions. While AI has great potential, it also holds great peril depending on how it is designed and used. AI for Diversity questions how AI technology can lead to inclusion or exclusion for diverse groups in society. The way data is selected, trained, used, and embedded into societies can have unfortunate consequences unless we critically investigate the dangers of systems left unchecked, and can lead to misogynistic, homophobic, racist, ageist, transphobic, or ableist outcomes. This book encourages the reader to take a step back to see how AI is impacting diverse groups of people and how diversity-awareness strategies can impact AI.

AI for Diversity (AI for Everything)

Artificial intelligence (AI) is increasingly impacting many aspects of people’s lives across the globe, from relatively mundane technology to more advanced digital systems that can make their own decisions. While AI has great potential, it also holds great peril depending on how it is designed and used. AI for Diversity questions how AI technology can lead to inclusion or exclusion for diverse groups in society. The way data is selected, trained, used, and embedded into societies can have unfortunate consequences unless we critically investigate the dangers of systems left unchecked, and can lead to misogynistic, homophobic, racist, ageist, transphobic, or ableist outcomes. This book encourages the reader to take a step back to see how AI is impacting diverse groups of people and how diversity-awareness strategies can impact AI.

AI for Behavioural Science

This book is a concise introduction to emerging concepts and ideas found at the intersection of contemporary behavioural science and artificial intelligence. The book explores how these disciplines interact, change, and adapt to one another and what the implications of such an interaction are for practice and society. AI for Behavioural Science book begins by exploring the field of machine behaviour, which advocates using behavioural science to investigate artificial intelligence. This perspective is built upon to develop a framework of terminology that treats humans and machines as comparable entities possessing their own motive power. From here, the notion of artificial intelligence systems becoming choice architects is explored through a series of reconceptualisations. The architecting of choices is reconceptualised as a process of selection from a set of choice architectural designs, while human behaviour is reconceptualised in terms of probabilistic outcomes. The material difference between the so-called "manual nudging" and "automatic nudging" (or hypernudging) is then explored. The book concludes with a discussion of who is responsible for autonomous choice architects.

AI for Behavioural Science

This book is a concise introduction to emerging concepts and ideas found at the intersection of contemporary behavioural science and artificial intelligence. The book explores how these disciplines interact, change, and adapt to one another and what the implications of such an interaction are for practice and society. AI for Behavioural Science book begins by exploring the field of machine behaviour, which advocates using behavioural science to investigate artificial intelligence. This perspective is built upon to develop a framework of terminology that treats humans and machines as comparable entities possessing their own motive power. From here, the notion of artificial intelligence systems becoming choice architects is explored through a series of reconceptualisations. The architecting of choices is reconceptualised as a process of selection from a set of choice architectural designs, while human behaviour is reconceptualised in terms of probabilistic outcomes. The material difference between the so-called "manual nudging" and "automatic nudging" (or hypernudging) is then explored. The book concludes with a discussion of who is responsible for autonomous choice architects.

AI-Driven Cybersecurity and Threat Intelligence: Cyber Automation, Intelligent Decision-Making and Explainability

This book explores the dynamics of how AI (Artificial Intelligence) technology intersects with cybersecurity challenges and threat intelligence as they evolve. Integrating AI into cybersecurity not only offers enhanced defense mechanisms, but this book introduces a paradigm shift illustrating how one conceptualize, detect and mitigate cyber threats. An in-depth exploration of AI-driven solutions is presented, including machine learning algorithms, data science modeling, generative AI modeling, threat intelligence frameworks and Explainable AI (XAI) models. As a roadmap or comprehensive guide to leveraging AI/XAI to defend digital ecosystems against evolving cyber threats, this book provides insights, modeling, real-world applications and research issues. Throughout this journey, the authors discover innovation, challenges, and opportunities. It provides a holistic perspective on the transformative role of AI in securing the digital world.Overall, the useof AI can transform the way one detects, responds and defends against threats, by enabling proactive threat detection, rapid response and adaptive defense mechanisms. AI-driven cybersecurity systems excel at analyzing vast datasets rapidly, identifying patterns that indicate malicious activities, detecting threats in real time as well as conducting predictive analytics for proactive solution. Moreover, AI enhances the ability to detect anomalies, predict potential threats, and respond swiftly, preventing risks from escalated. As cyber threats become increasingly diverse and relentless, incorporating AI/XAI into cybersecurity is not just a choice, but a necessity for improving resilience and staying ahead of ever-changing threats. This book targets advanced-level students in computer science as a secondary textbook. Researchers and industry professionals working in various areas, such as Cyber AI, Explainable and Responsible AI, Human-AI Collaboration, Automation and Intelligent Systems, Adaptive and Robust Security Systems, Cybersecurity Data Science and Data-Driven Decision Making will also find this book useful as reference book.

AI Assisted Business Analytics: Techniques for Reshaping Competitiveness

The primary path to success, is to use software designed to sample and analyze cashflow and then link that analysis, with forecasting and market research. The case study will start with a small business income statement indicating a cashflow problem. The analysis that follows will be a comprehensive statistical approach of fiscal management. The case study will provide an overview of the total process of controlling and analyzing cashflow. Business prosperity depends on: 1- Staying in touch with cashflow by means of regular statistical audits2- Transition to statistical methods for forecasting future cashflow3- Link cashflow with customer perception and satisfaction The book is intended for courses with prerequisites that the student has a knowledge of accounting and is comfortable in using Excel. It uses professional Excel with its Analytics Toolkit. Complete knowledge of the Toolkit is not a prerequisite since the book will adequately cover the relevant analytic tools. There is no need for separate statistical software such as SPSS or SAS. The book is intended for intermediate/advanced college level courses in business financial methods and control.

AI Approaches to the Complexity of Legal Systems XI-XII: AICOL International Workshops 2018 and 2020: AICOL-XI@JURIX 2018, AICOL-XII@JURIX 2020, XAILA@JURIX 2020, Revised Selected Papers (Lecture Notes in Computer Science #13048)

This book includes revised selected papers from the International Workshops on AI Approaches to the Complexity of Legal Systems, AICOL-XI@JURIX2018, held in Groningen, The Netherlands, on December 12, 2018; AICOL-XII@JURIX 2020, held in Brno, Czechia, on December 9, 2020; XAILA@JURIX 2020, held in in Brno, Czechia, on December 9, 2020.*The 17 full and 4 short papers included in this volume were carefully reviewed and selected form 39 submissions. They represent a comprehensive picture of the state of the art in legal informatics. The papers are logically organized in 5 blocks: ​Knowledge Representation; Logic, rules, and reasoning; Explainable AI in Law and Ethics; Law as Web of linked Data and the Rule of Law; Data protection and Privacy Modelling and Reasoning.*Due to the Covid-19 pandemic AICOL-XII@JURIX 2020 and XAILA@JURIX 2020 were held virtually.

AI Approaches to the Complexity of Legal Systems: International Workshops Aicol-i/ivr-xxiv, Beijing, China, September 19, 2009 And Aicol-ii/jurix 2009, Rotterdam, The Netherlands, December 16, 2009 Revised Selected Papers (Lecture Notes in Computer Science #6237)

This book includes revised selected papers from five International Workshops on Artificial Intelligence Approaches to the Complexity of Legal Systems, AICOL VI to AICOL X, held during 2015-2017: AICOL VI in Braga, Portugal, in December 2015 as part of JURIX 2015; AICOL VII at EKAW 2016 in Bologna, Italy, in November 2016; AICOL VIII in Sophia Antipolis, France, in December 2016; AICOL IX at ICAIL 2017 in London, UK, in June 2017; and AICOL X as part of JURIX 2017 in Luxembourg, in December 2017.The 37 revised full papers included in this volume were carefully reviewed and selected form 69 submissions. They represent a comprehensive picture of the state of the art in legal informatics. The papers are organized in six main sections: legal philosophy, conceptual analysis, and epistemic approaches; rules and norms analysis and representation;legal vocabularies and natural language processing; legal ontologies and semantic annotation; legal argumentation; and courts, adjudication and dispute resolution.

AI and Metaverse: Volume 2 (Studies in Computational Intelligence #1160)

The book reports the state-of-the-art results in Artificial Intelligence and Metaverse in both printed and electronic form. Studies in Computation Intelligence (SCI) has grown into the most comprehensive computational intelligence research forum available in the world. This book publishes original papers on both theory and practice that address foundations, state-of-the-art problems and solutions, and crucial challenges.

AI and Analytics for Smart Cities and Service Systems: Proceedings of the 2021 INFORMS International Conference on Service Science (Lecture Notes in Operations Research)

This book showcases state-of-the-art advances in service science and related fields of research, education, and practice. It presents emerging technologies and applications in contexts ranging from healthcare, energy, finance, and information technology to transportation, sports, logistics, and public services. Regardless of its size and service, every service organization is a service system. Due to the socio-technical nature of service systems, a systems approach must be adopted in order to design, develop and deliver services aimed at meeting end users’ utilitarian and socio-psychological needs alike. Understanding services and service systems often requires combining multiple methods to consider how interactions between people, technologies, organizations and information create value under various conditions. The papers in this volume highlight a host of ways to approach these challenges in service science and are based on submissions to the 2021 INFORMS Conference on Service Science.

AI and Analytics for Public Health: Proceedings of the 2020 INFORMS International Conference on Service Science (Springer Proceedings in Business and Economics)

This volume offers the state-of-the-art research and developments in service science and related research, education and practice areas. It showcases emerging technology and applications in fields including healthcare, energy, finance, information technology, transportation, sports, logistics, and public services. Regardless of size and service, a service organization is a service system. Because of the socio-technical nature of a service system, a systems approach must be adopted to design, develop, and deliver services, aimed at meeting end users’ both utilitarian and socio-psychological needs. Effective understanding of service and service systems often requires combining multiple methods to consider how interactions of people, technology, organizations, and information create value under various conditions. Chapters highlight ways to approach such technical challenges in service science and are based on submissions from the 2020 INFORMS International Conference on Service Science.

AI 2023: 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, November 28–December 1, 2023, Proceedings, Part I (Lecture Notes in Computer Science #14471)

This two-volume set LNAI 14471-14472 constitutes the refereed proceedings of the 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, held in Brisbane, QLD, Australia during November 28 – December 1, 2023. The 23 full papers presented together with 59 short papers were carefully reviewed and selected from 213 submissions. They are organized in the following topics: computer vision; deep learning; machine learning and data mining; optimization; medical AI; knowledge representation and NLP; explainable AI; reinforcement learning; and genetic algorithm.

AI 2023: 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, November 28–December 1, 2023, Proceedings, Part II (Lecture Notes in Computer Science #14472)

This two-volume set LNAI 14471-14472 constitutes the refereed proceedings of the 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, held in Brisbane, QLD, Australia during November 28 – December 1, 2023. The 23 full papers presented together with 59 short papers were carefully reviewed and selected from 213 submissions. They are organized in the following topics: computer vision; deep learning; machine learning and data mining; optimization; medical AI; knowledge representation and NLP; explainable AI; reinforcement learning; and genetic algorithm..

AI 2022: 35th Australasian Joint Conference, AI 2022, Perth, WA, Australia, December 5–8, 2022, Proceedings (Lecture Notes in Computer Science #13728)

This book constitutes the refereed proceedings of the 35th Australasian Joint Conference on Artificial Intelligence, AI 2022, which took place in Perth, WA, Australia, in December 5–8, 2022. The 56 full papers included in this book were carefully reviewed and selected from 90 submissions. They were organized in topical sections as follows: Computer Vision; Deep Learning; Ethical/Explainable AI; Genetic Algorithms; Knowledge Representation and NLP; Machine Learning; Medical AI; Optimization; and Reinforcement Learning.

AI 2015: 28th Australasian Joint Conference, Canberra, ACT, Australia, November 30 -- December 4, 2015, Proceedings (Lecture Notes in Computer Science #9457)

This book constitutes the refereed proceedings of the 28th Australasian Joint Conference on Artificial Intelligence, AI 2015, held in Canberra, Australia, in November/December 2015. The 39 full papers and 18 short papers presented were carefully reviewed and selected from 102 submissions.

AI 2008: 21st Australasian Joint Conference on Artificial Intelligence, Auckland, New Zealand, December 3-5, 2008, Proceedings (Lecture Notes in Computer Science #5360)

This book constitutes the refereed proceedings of the 21th Australasian Joint Conference on Artificial Intelligence, AI 2008, held in Auckland, New Zealand, in December 2008. The 42 revised full papers and 21 revised short papers presented together with 1 invited lecture were carefully reviewed and selected from 143 submissions. The papers are organized in topical sections on knowledge representation, constraints, planning, grammar and language processing, statistical learning, machine learning, data mining, knowledge discovery, soft computing, vision and image processing, and AI applications.

AI 2007: 20th Australian Joint Conference on Artificial Intelligence, Gold Coast, Australia, December 2-6, 2007, Proceedings (Lecture Notes in Computer Science #4830)

This book constitutes the refereed proceedings of the 20th Australian Joint Conference on Artificial Intelligence, AI 2007, held in Gold Coast, Australia, in December 2007.The 58 revised full papers and 40 revised short papers presented together with the extended abstracts of three invited speeches were carefully reviewed and selected from 194 submissions. The papers are organized in topical sections on a broad range of subjects.

AI 2006: 19th Australian Joint Conference on Artificial Intelligence, Hobart, Australia, December 4-8, 2006, Proceedings (Lecture Notes in Computer Science #4304)

This book constitutes the refereed proceedings of the 19th Australian Joint Conference on Artificial Intelligence, AI 2006, held in Hobart, Australia, December 2006. Coverage includes foundations and knowledge based system, machine learning, connectionist AI, data mining, intelligent agents, cognition and user interface, vision and image processing, natural language processing and Web intelligence, neural networks, robotics, and AI applications.

AI 2005: 18th Australian Joint Conference on Artificial Intelligence, Sydney, Australia, December 5-9, 2005, Proceedings (Lecture Notes in Computer Science #3809)

The 18th Australian Joint Conference on Artificial Intelligence (AI 2005) was held at the University of Technology, Sydney (UTS), Sydney, Australia from 5 to 9 December 2005. AI 2005 attracted a historical record number of submissions, a total of 535 papers. The review process was extremely selective. Out of these 535 submissions, the Program Chairs selected only 77 (14.4%) full papers and 119 (22.2%) short papers based on the review reports, making an acceptance rate of 36.6% in total. Authors of the accepted papers came from over 20 countries. This volume of the proceedings contains the abstracts of three keynote speeches and all the full and short papers. The full papers were categorized into three broad sections, namely: AI foundations and technologies, computational intelligence, and AI in specialized domains. AI 2005 also hosted several tutorials and workshops, providing an interacting mode for specialists and scholars from Australia and other countries. Ronald R. Yager, Geoff Webb and David Goldberg (in conjunction with ACAL05) were the distinguished researchers invited to give presentations. Their contributions to AI 2005 are really appreciated.

AI 2004: 17th Australian Joint Conference on Artificial Intelligence, Cairns, Australia, December 4-6, 2004, Proceedings (Lecture Notes in Computer Science #3339)