For the second year in a row, we are very happy to offer our readership an ebook of 10 articles that have achieved widespread acceptance within our core audience and beyond. This time it concerns articles published in 2023, a landmark year for this journal, as it was officially awarded its first impact factor. These papers are among the large number that attained significant interest last year, but we selected just 10, which we consider to be the “best”. These articles have already made an impact in the form of original research or comprehensive reviews. As the Field Chief Editor, I would like to stand alongside our journal staff to honor all authors who contributed very high-level papers to the journal last year and are contributing to our success. We also thank the editors and reviewers of these papers, and of all papers this past year, for their invaluable contribution.

Social institutions emerge from social practices which coordinate activities by the explicit statement of rules, goals, and values. When artificial social actors are introduced into the physical and symbolic space of institutions, will this affect or transform institutional structures and practices, and how can social robotics as an interdisciplinary endeavor contribute to the ability of our institutions to perform their functions in society? This book presents the proceedings of Robophilosophy 2022, the 5th in the biennial Robophilosophy conference series, held in Helsinki, Finland, from 16 to 19 August 2022. The theme of this edition of the conference was Social Robots in Social Institutions, and it featured international multidisciplinary research from the humanities and social sciences concerning social robotics. The 63 papers, 41 workshop papers and 5 posters included in this book are divided into 4 sections: plenaries, sessions, workshops and posters, with the 41 papers in the ‘Sessions’ section grouped into 13 subdivisions including elderly care, healthcare, law, education and art, as well as ethics and religion. These papers explore the anticipated conceptual and practical changes which will come about from the introduction of social robotics into public and private institutions, such as public services, legal systems, social and healthcare services or educational institutions. Offering an exploration of the societal significance of social robots for the future of social institutions, the book will be of interest to both researchers in robotics and to those working in social institutions and enterprises.

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.

The Agricultural industry is always at risk - crops are affected by weather, diseases and pests. When a global pandemic hits suddenly, it becomes very difficult to manage various processes because most are not digital. In parallel, the rapid increase in the population and urbanization, demands more food production on less land. Farming under the demand pressure by increasing input consumption, leads to increases costs and negative impacts to the environment such as decreasing soil fertility continuously over time. There is therefore the need to move beyond traditional farming. To produce more yield in the agricultural field, there is the need to dig deep into the technological field and apply sensors, Internet of Things (IoT), big data analytics, cloud computing and machine learning (ML) and deep learning (DL) techniques. Better decision making, prediction and reliability depend upon high level of knowledge base and perceptions.

Explains the theory behind Machine Learning and highlights how Mathematics can be used in Artificial Intelligence Illustrates how to improve existing algorithms by using advanced mathematics and discusses how Machine Learning can support mathematical modeling Captures how to simulate data by means of artificial neural networks and offers cutting-edge Artificial Intelligence technologies Emphasizes the classification of algorithms, optimization methods, and statistical techniques Explores future integration between Machine Learning and complex mathematical techniques

Even before the deep learning revolution, the landscape of artificial intelligence (AI) was already changing drastically in the 90s. Embodied intelligence, it was proposed, must play a crucial role in the design of intelligent machines. This new wave was inspired by what is today known as Embodied and Enactive Cognitive Science or E-Cognition, which considers that cognitive activity does not reduce to the intellectual capacities of agents being able to represent their environments. E-cognition set AI and robotics in a new direction, in which intelligent machines are required to interact with the environment, and where this interaction does not reduce to explicit representations or prespecified algorithms. These ideas revolutionized the way we think about intelligent machines and cognition, but these theoretical advances are only partially reflected in modern approaches to AI and machine learning (ML). Despite deeply impressive achievements, AI/ML still struggles to recapitulate the kinds of intelligence we find in natural systems, whether we are considering individual insects (e.g. simultaneous localization and mapping), or swarm behaviour (e.g. forum sensing and ensemble inferences), and especially the kinds of flexibility and high-level reasoning characteristic of human cognition.