Artificial life is now a recognized discipline of research with many important applications and software tools. However, many theoretical issues remain unresolved. This book brings together a cross-section of key developments in artificial life, which in turn gives us new insight into the theory of complex systems.The central ideas of the book surround genetics and evolution in an artificial life framework. Topics covered include maintenance of genetic diversity, hierarchical structures and stability of ecosystems. Underpinning these topics are key theoretical developments surrounding network complexity, the development of pattern languages for complex networks and a deeper understanding of the edge of chaos where complex systems live. Practical applications include optimization, gene regulatory networks, modeling the spread of disease and the evolution of ageing.The reader will gain an insight into the mathematical techniques at the core of artificial life and encounter a sufficient diversity of applications to stimulate new directions in their own field.

This book constitutes the refereed proceedings of the 7th European Conference on Artificial Life, ECAL 2003, held in Dortmund, Germany in September 2003. The 96 revised full papers presented were carefully reviewed and selected from more than 140 submissions. The papers are organized in topical sections on artificial chemistries, self-organization, and self-replication; artificial societies; cellular and neural systems; evolution and development; evolutionary and adaptive dynamics; languages and communication; methodologies and applications; and robotics and autonomous agents.

This book presents select proceedings of the International Conference on Artificial Intelligence and Data Engineering (AIDE 2020). Various topics covered in this book include deep learning, neural networks, machine learning, computational intelligence, cognitive computing, fuzzy logic, expert systems, brain-machine interfaces, ant colony optimization, natural language processing, bioinformatics and computational biology, cloud computing, machine vision and robotics, ambient intelligence, intelligent transportation, sensing and sensor networks, big data challenge, data science, high performance computing, data mining and knowledge discovery, and data privacy and security. The book will be a valuable reference for beginners, researchers, and professionals interested in artificial intelligence, robotics and data engineering.

"This book provides the latest research, investigation, and development in the area of living systems intelligence, human-level cognition & artificial systems, nature and bioinspiration, machine learning techniques, Deep Learning techniques and applications, and systems that exhibit intelligent autonomous behavioral characteristics"--

An examination of machine learning art and its practice in new media art and music. Over the past decade, an artistic movement has emerged that draws on machine learning as both inspiration and medium. In this book, transdisciplinary artist-researcher Sofian Audry examines artistic practices at the intersection of machine learning and new media art, providing conceptual tools and historical perspectives for new media artists, musicians, composers, writers, curators, and theorists. Audry looks at works from a broad range of practices, including new media installation, robotic art, visual art, electronic music and sound, and electronic literature, connecting machine learning art to such earlier artistic practices as cybernetics art, artificial life art, and evolutionary art. Machine learning underlies computational systems that are biologically inspired, statistically driven, agent-based networked entities that program themselves. Audry explains the fundamental design of machine learning algorithmic structures in terms accessible to the nonspecialist while framing these technologies within larger historical and conceptual spaces. Audry debunks myths about machine learning art, including the ideas that machine learning can create art without artists and that machine learning will soon bring about superhuman intelligence and creativity. Audry considers learning procedures, describing how artists hijack the training process by playing with evaluative functions; discusses trainable machines and models, explaining how different types of machine learning systems enable different kinds of artistic practices; and reviews the role of data in machine learning art, showing how artists use data as a raw material to steer learning systems and arguing that machine learning allows for novel forms of algorithmic remixes.

This book collects recent theoretical advances and concrete applications of learning automata (LAs) in various areas of computer science, presenting a broad treatment of the computer science field in a survey style. Learning automata (LAs) have proven to be effective decision-making agents, especially within unknown stochastic environments. The book starts with a brief explanation of LAs and their baseline variations. It subsequently introduces readers to a number of recently developed, complex structures used to supplement LAs, and describes their steady-state behaviors. These complex structures have been developed because, by design, LAs are simple units used to perform simple tasks; their full potential can only be tapped when several interconnected LAs cooperate to produce a group synergy. In turn, the next part of the book highlights a range of LA-based applications in diverse computer science domains, from wireless sensor networks, to peer-to-peer networks, to complex social networks, and finally to Petri nets. The book accompanies the reader on a comprehensive journey, starting from basic concepts, continuing to recent theoretical findings, and ending in the applications of LAs in problems from numerous research domains. As such, the book offers a valuable resource for all computer engineers, scientists, and students, especially those whose work involves the reinforcement learning and artificial intelligence domains.

This book constitutes the revised selected papers of the 10th Italian Workshop on Advances in Artificial Life, Evolutionary Computation and Systems Chemistry, WIVACE 2015, held at Bari, Italy, in September 2015. The 18 papers presented have been thoroughly reviewed and selected from 45 submissions. They cover the following topics: evolutionary computation, bioinspired algorithms, genetic algorithms, bioinformatics and computational biology, modeling and simulation of artificial and biological systems, complex systems, synthetic and systems biology, systems chemistry.