This book is an up-to-date collection, in AI and environmental research, related to the project ATLAS. AI is used for gaining an understanding of complex research phenomena in the environmental sciences, encompassing heterogeneous, noisy, inaccurate, uncertain, diverse spatio-temporal data and processes. The first part of the book covers new mathematics in the field of AI: aggregation functions with special classes such as triangular norms and copulas, pseudo-analysis, and the introduction to fuzzy systems and decision making. Generalizations of the Choquet integral with applications in decision making as CPT are presented. The second part of the book is devoted to AI in the geo-referenced air pollutants and meteorological data, image processing, machine learning, neural networks, swarm intelligence, robotics, mental well-being and data entry errors. The book is intended for researchers in AI and experts in environmental sciences as well as for Ph.D. students.

One of the goals of artificial intelligence (AI) is creating autonomous agents that must make decisions based on uncertain and incomplete information. The goal is to design rational agents that must take the best action given the information available and their goals. Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions provides an introduction to different types of decision theory techniques, including MDPs, POMDPs, Influence Diagrams, and Reinforcement Learning, and illustrates their application in artificial intelligence. This book provides insights into the advantages and challenges of using decision theory models for developing intelligent systems.

Artificial Intelligence in Data Mining: Theories and Applications offers a comprehensive introduction to data mining theories, relevant AI techniques, and their many real-world applications. This book is written by experienced engineers for engineers, biomedical engineers, and researchers in neural networks, as well as computer scientists with an interest in the area. - Provides coverage of the fundamentals of Artificial Intelligence as applied to data mining, including computational intelligence and unsupervised learning methods for data clustering - Presents coverage of key topics such as heuristic methods for data clustering, deep learning methods for data classification, and neural networks - Includes case studies and real-world applications of AI techniques in data mining, for improved outcomes in clinical diagnosis, satellite data extraction, agriculture, security and defense

This book examines the fundamentals and technologies of Artificial Intelligence (AI) and describes their tools, challenges, and issues. It also explains relevant theory as well as industrial applications in various domains, such as healthcare, economics, education, product development, agriculture, human resource management, environmental management, and marketing. The book is a boon to students, software developers, teachers, members of boards of studies, and researchers who need a reference resource on artificial intelligence and its applications and is primarily intended for use in courses offered by higher education institutions that strive to equip their graduates with Industry 4.0 skills. FEATURES: Gender disparity in the enterprises involved in the development of AI-based software development as well as solutions to eradicate such gender bias in the AI world A general framework for AI in environmental management, smart farming, e-waste management, and smart energy optimization The potential and application of AI in medical imaging as well as the challenges of AI in precision medicine AI’s role in the diagnosis of various diseases, such as cancer and diabetes The role of machine learning models in product development and statistically monitoring product quality Machine learning to make robust and effective economic policy decisions Machine learning and data mining approaches to provide better video indexing mechanisms resulting in better searchable results ABOUT THE EDITORS: Prof. Dr. P. Kaliraj is Vice Chancellor at Bharathiar University, Coimbatore, India. Prof. Dr. T. Devi is Professor and Head of the Department of Computer Applications, Bharathiar University, Coimbatore, India.

This book constitutes the refereed proceedings of the 5th Hellenic Conference on Artificial Intelligence, SETN 2008, held at Syros, Greece in October 2008. The 27 revised full papers together with 17 revised short papers were carefully reviewed and selected from 76 submissions. The papers address any area of artificial intelligence; particular fields of interest include: Adaptive Systems, AI and Creativity, AI rchitectures, Artificial Life, Autonomous Systems, Data Mining and Knowledge Discovery, Hybrid Intelligent Systems & Methods, Intelligent Agents, Multi-agent Systems, Intelligent Distributed Systems, Intelligent Information Retrieval, Intelligent/Natural Interactivity, Intelligent Virtual Environments, Knowledge Representation and Reasoning, Logic Programming, Knowledge-Based Systems, Machine Learning, Neural Nets, Genetic Algorithms, Natural Language Processing, Planning and Scheduling, Problem Solving, Constraint Satisfaction, Robotics, Machine Vision, Machine Sensing.

Presents extensions of papers reported in the proceedings of the 1989 SPIE symposium. In the applications group articles cover the use of expert systems in computer aided education, hydrocarbon (oil) exploration, satellite image analysis for oceanography, particle beam accelerator tuning, design of internal combustion engines. Papers in the theory group cover theories, algorithms, architectures and software tools that can be used for modules within future systems.

This book features a collection of high-quality research papers presented at International Conference on Artificial Intelligence: Theory and Applications (AITA 2023), held during 11–12 August 2023 in Bengaluru, India. The book is divided into two volumes and presents original research and review papers related to artificial intelligence and its applications in various domains including health care, finance, transportation, education, and many more.

This book provides a detailed understanding of the broad issues in artificial intelligence and a survey of current AI technology. The author delivers broad coverage of innovative representational techniques, including neural networks, image processing and probabilistic reasoning, alongside the traditional methods of symbolic reasoning. The work is intended for students in artificial intelligence, researchers and LISP programmers.

A comprehensive introduction to new approaches in artificial intelligence and robotics that are inspired by self-organizing biological processes and structures. New approaches to artificial intelligence spring from the idea that intelligence emerges as much from cells, bodies, and societies as it does from evolution, development, and learning. Traditionally, artificial intelligence has been concerned with reproducing the abilities of human brains; newer approaches take inspiration from a wider range of biological structures that that are capable of autonomous self-organization. Examples of these new approaches include evolutionary computation and evolutionary electronics, artificial neural networks, immune systems, biorobotics, and swarm intelligence—to mention only a few. This book offers a comprehensive introduction to the emerging field of biologically inspired artificial intelligence that can be used as an upper-level text or as a reference for researchers. Each chapter presents computational approaches inspired by a different biological system; each begins with background information about the biological system and then proceeds to develop computational models that make use of biological concepts. The chapters cover evolutionary computation and electronics; cellular systems; neural systems, including neuromorphic engineering; developmental systems; immune systems; behavioral systems—including several approaches to robotics, including behavior-based, bio-mimetic, epigenetic, and evolutionary robots; and collective systems, including swarm robotics as well as cooperative and competitive co-evolving systems. Chapters end with a concluding overview and suggested reading.