A fundamental goal of artificial intelligence research and development is the creation of machines that demonstrate what humans consider to be intelligent behavior. Effective knowledge representation and reasoning (KR&R) methods are a foundational requirement for intelligent machines. The development of these methods remains a rich and active area of artificial intelligence research in which advances have been motivated by many factors, including interest in new challenge problems, interest in more complex domains, shortcomings of current methods, improved computational support, increases in requirements to interact effectively with humans, and ongoing funding from Defense Advanced Research Projects Agency and other agencies. The article by Richard Fikes and Tom Garvey, Knowledge Representation and Reasoning – A History of DARPA Leadership, highlights several decades of advances in KR&R, paying particular attention to research on planning and on the impact of DARPA's support. Fikes and Garvey are joined by David Israel, a principal scientist in the Artificial Intelligence Center at SRI International, who provides his own brief commentary on KR&R.

This book discusses the necessity and perhaps urgency for the regulation of algorithms on which new technologies rely; technologies that have the potential to re-shape human societies. From commerce and farming to medical care and education, it is difficult to find any aspect of our lives that will not be affected by these emerging technologies. At the same time, artificial intelligence, deep learning, machine learning, cognitive computing, blockchain, virtual reality and augmented reality, belong to the fields most likely to affect law and, in particular, administrative law. The book examines universally applicable patterns in administrative decisions and judicial rulings. First, similarities and divergence in behavior among the different cases are identified by analyzing parameters ranging from geographical location and administrative decisions to judicial reasoning and legal basis. As it turns out, in several of the cases presented, sources of general law, such as competition or labor law, are invoked as a legal basis, due to the lack of current specialized legislation. This book also investigates the role and significance of national and indeed supranational regulatory bodies for advanced algorithms and considers ENISA, an EU agency that focuses on network and information security, as an interesting candidate for a European regulator of advanced algorithms. Lastly, it discusses the involvement of representative institutions in algorithmic regulation.

Various artificial intelligence (AI) technologies have pervaded daily life. For instance, speech recognition has enabled users to interact with a system using their voice, and recent advances in computer vision have made self-driving cars commercially available. However, if not carefully designed, people with different abilities (e.g., loss of vision, weak technical background) may not receive full benefits from these AI-based approaches. This Special Issue focuses on bridging or closing the information gap between people with disabilities and needs. Manuscripts should be submitted online at www.mdpi.com

The Coronavirus Disease 2019 (COVID-19) pandemic has caused extreme strains on health systems, public health infrastructure, and economies of many countries. As of April 2020, millions of people have been infected, and more than two billion globally are staying home to avoid coronavirus. This raging pandemic continues to disrupt our lives while the scientific community is rushing to find a cure for COVID-19. We can leverage artificial intelligence (AI) and big data to help combat the COVID-19 pandemic. For example, ongoing AI efforts aim to expedite the development of a safe and effective COVID-19 vaccine, use networks to repurpose drugs for COVID-19, predict antibacterial properties of new molecules, and design machine-readable datasets of scientific literature on the novel coronavirus.

This Special Issue is devoted to the recent advances in prediction models. Novel methods, new applications, comparative analyses of models, case studies, and state-of-the-art review papers are particularly welcomed. Prediction models are essential to many scientific domains and are gaining widespread popularity. Health care, cybersecurity, education, credit card fraud detection, social media, cloud computing, software measurement, quality and defect simulation, cost and effort estimations, software reuse and evaluation, computational mechanics, theoretical physics, astrophysics, materials design innovation, disease diagnosis, hydrological modeling, earth systems, atmospheric sciences, weather and extreme events prediction, hazard mapping, natural disasters warning systems, policy-making, energy systems, time-series forecasting, and climate change modeling are among the popular applications of prediction models in the literature. The beneficial aspects and the generalizability of prediction models in various technological and scientific domains have highly increased the progression, competitiveness, and research impact of different fields.

As a Principal Automation Architect at Magenic, Paul Grizzaffi is following his passion for providing technology solutions to testing, QE, and QA organizations, including automation assessments, implementations, and through activities benefiting the broader testing community. An accomplished keynote speaker and writer, Paul has spoken at local and national conferences and meetings. In addition to spending time with his twins, Paul enjoys sharing his experiences and learning from other testing professionals; his mostly cogent thoughts can be read on his blog. Jennifer Bonine is a well-known speaker, teacher, and trainer at both national and international conferences. She has keynoted numerous Testing, Agile, and Development conferences.

Artificial intelligence and machine learning are disrupting fields of technology. We hear about all the latest tech, advanced implementations in various industries every day. These news stories amaze and scare all at the same. It demonstrates what technology can help us to achieve but due to the ambiguity around, we are skeptical about adopting it in our own everyday lives. But once we grasp the concepts that form the core of machine learning and artificial intelligence, we will be able to look at these technologies with new lens and perspective.

As is well known, machine learning (ML) is one of the main branches of artificial intelligence (AI). Its primary objective is to use computational methods to extract information from data. Machine learning has a wide spectrum of practical applications. After the first applications concerning topics such as recognition of manual writing, detection of objects in image processing, voice recognition, medical diagnoses, DNA classification, search engines, and stock market analysis, in recent years machine learning algorithms have been increasingly used in environmental sciences due to their high capability for modelling non-linear phenomena. In particular, these algorithms are already widely used in weather and climate forecasts, as well as in the analysis and modelling of hydrological, ecological, and oceanographic data.

AIIA 2019 is organized by the Italian Association for Artificial Intelligence (AIIA – Associazione Italiana per l'Intelligenza Artificiale), which is a non-profit scientific society founded in 1988 devoted to the promotion of Artificial Intelligence. The society aims to increase the public awareness of AI, encourage the teaching of it and promote research in the field.

Object-oriented programming (OOP) is the foundation of modern programming languages, including C, Java, C#, Visual Basic .NET, Ruby, and Objective-C.