Computational Argumentation A Foundation For Human Centric Ai
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Computational Argumentation: a Foundation for Human-centric AI
Human-centric AI aims at designing and developing systems that operate along with humans in a cognitively-compatible and synergetic way. Such systems are required to exhibit human-like cognitive abilities and intelligence, either at the general level of the human population or at some specialized level of expertise in a specific field. In either case, human-centric AI systems exist as (expert) companions or peers of their human users that would operate alongside them to support and enhance their capabilities.
Human-Centered Artificial Intelligence
The 18th European Advanced Course on AI (ACAI) took place in Berlin on 11-15 October 2021, organized by the European project Humane-AI Net in collaboration with the European AI Association (EURAI). The school included tutorials on different topics, which were selected through an open call to top European AI researchers. In addition, the school also included 4 invited talks, a student poster presentation, and a mentorship program. This volume contains 21 tutorial chapters organized according to the following themes: human-centered AI; human-centered machine learning; explainable AI; ethics, law, and the societal aspects of AI; argumentation; and social simulation. The contributions include learning objectives, reading lists, and links to further resources.
Compendium of Neurosymbolic Artificial Intelligence
If only it were possible to develop automated and trainable neural systems that could justify their behavior in a way that could be interpreted by humans like a symbolic system. The field of Neurosymbolic AI aims to combine two disparate approaches to AI; symbolic reasoning and neural or connectionist approaches such as Deep Learning. The quest to unite these two types of AI has led to the development of many innovative techniques which extend the boundaries of both disciplines. This book, Compendium of Neurosymbolic Artificial Intelligence, presents 30 invited papers which explore various approaches to defining and developing a successful system to combine these two methods. Each strategy has clear advantages and disadvantages, with the aim of most being to find some useful middle ground between the rigid transparency of symbolic systems and the more flexible yet highly opaque neural applications. The papers are organized by theme, with the first four being overviews or surveys of the field. These are followed by papers covering neurosymbolic reasoning; neurosymbolic architectures; various aspects of Deep Learning; and finally two chapters on natural language processing. All papers were reviewed internally before publication. The book is intended to follow and extend the work of the previous book, Neuro-symbolic artificial intelligence: The state of the art (IOS Press; 2021) which laid out the breadth of the field at that time. Neurosymbolic AI is a young field which is still being actively defined and explored, and this book will be of interest to those working in AI research and development.