In recent years, there has been a proliferation of technological developments that incorporate processing of human language. Hardware and software can be specialized for designated subject areas, and computational devices are designed for a widening variety of applications. At the same time, new areas and applications are emerging by demanding intelligent technology enhanced by the processing of human language. These new applications often perform tasks which handle information, and they have a capacity to reason, using both formal and human language. Many sub-areas of Artificial Intelligence demand integration of Natural Language Processing, at least to some degree. Furthermore, technologies require coverage of known as well as unknown agents, and tasks with potential variations. All of this takes place in environments with unknown factors. The book covers theoretical work, advanced applications, approaches, and techniques for computational models of information, reasoning systems, and presentation in language. The book promotes work on intelligent natural language processing and related models of information, thought, reasoning, and other cognitive processes. The topics covered by the chapters prompt further research and developments of advanced systems in the areas of logic, computability, computational linguistics, cognitive science, neuroscience of language, robotics, and artificial intelligence, among others.

This book contains the revised and extended versions of selected papers from the 10th International Conference, ICAART 2018, held in Funchal, Madeira, Portugal, in January 2018. The 45 full papers together with 42 short papers and 26 Posters were carefully reviewed and selected from 161 initial submissions. The papers are organized in topics such as Agents, Artificial Intelligence, Semantic Web, Multi-Agent Systems, Distributed Problem Solving, Agent Communication and much more.

This book assesses the place of logic, mathematics, and computer science in present day, interdisciplinary areas of computational linguistics. Computational linguistics studies natural language in its various manifestations from a computational point of view, both on the theoretical level (modeling grammar modules dealing with natural language form and meaning and the relation between these two) and on the practical level (developing applications for language and speech technology). It is a collection of chapters presenting new and future research. The book focuses mainly on logical approaches to computational processing of natural language and on the applicability of methods and techniques from the study of formal languages, programming, and other specification languages. It presents work from other approaches to linguistics, as well, especially because they inspire new work and approaches.

The special session on Decision Economics (DECON) is a scientific forum held annually, which is focused on sharing ideas, projects, research results, models, and experiences associated with the complexity of behavioural decision processes and socio‐economic phenomena. In 2018, DECON was held at Campus Tecnológico de la Fábrica de Armas, University of Castilla-La Mancha, Toledo, Spain, as part of the 15th International Conference on Distributed Computing and Artificial Intelligence. For the third consecutive year, this book have drawn inspiration from Herbert A. Simon’s interdisciplinary legacy and, in particular, is devoted to designs, models, and techniques for boundedly rational decisions, involving several fields of study and expertise. It is worth noting that the recognition of relevant decision‐making takes place in a range of critical subject areas and research fields, including economics, finance, information systems, small and international business management, operations, and production. Therefore, decision‐making issues are of fundamental importance in all branches of economics addressed with different methodological approaches. As a matter of fact, the study of decision‐making has become the focus of intense research efforts, both theoretical and applied, forming a veritable bridge between theory and practice as well as science and business organisations, whose pillars are based on insightful cutting‐edge experimental, behavioural, and computational approaches on the one hand, and celebrating the value of science as well as the close relationship between economics and complexity on the other. In this respect, the international scientific community acknowledges Herbert A. Simon’s research endeavours to understand the processes involved in economic decision‐making and their implications for the advancement of economic professions. Within the field of decision‐making, indeed, Simon has become a mainstay of bounded rationality and satisficing. His rejection of the standard (unrealistic) decision‐making models adopted by neoclassical economists inspired social scientists worldwide with the purpose to develop research programmes aimed at studying decision‐making empirically, experimentally, and computationally. The main achievements concern decision‐making for individuals, firms, markets, governments, institutions, and, last but not least, science and research. This book of selected papers tackles these issues that Simon broached in a professional career spanning more than sixty years. The Editors of this book dedicated it to Herb.

DCAI 2022 is a forum to present applications of innovative techniques for studying and solving complex problems in artificial intelligence and computing areas. The present edition brings together past experience, current work and promising future trends associated with distributed computing, artificial intelligence and their application in order to provide efficient solutions to real problems. This year’s technical program will present both high quality and diversity, with contributions in well-established and evolving areas of research. Specifically, 61 papers were submitted, by authors from 28 different countries representing a truly “wide area network” of research activity. The DCAI’22 technical program has selected 32 full papers and, as in past editions, it will be special issues in ranked journals. This symposium is organized by the University of L'Aquila (Italy). We would like to thank all the contributing authors, the members of the Program Committee and the sponsors (IBM, Indra, Dipartimento di Ingegneria e Scienze dell'Informazione e Matematica dell'Università degli Studi dell'Aquila, Armundia Group, Whitehall Reply, T.C. Technologies And Comunication S.R.L., LCL Industria Grafica, AIR Institute, AEPIA, APPIA).

Algebraic Structures in Natural Language addresses a central problem in cognitive science concerning the learning procedures through which humans acquire and represent natural language. Until recently algebraic systems have dominated the study of natural language in formal and computational linguistics, AI, and the psychology of language, with linguistic knowledge seen as encoded in formal grammars, model theories, proof theories and other rule-driven devices. Recent work on deep learning has produced an increasingly powerful set of general learning mechanisms which do not apply rule-based algebraic models of representation. The success of deep learning in NLP has led some researchers to question the role of algebraic models in the study of human language acquisition and linguistic representation. Psychologists and cognitive scientists have also been exploring explanations of language evolution and language acquisition that rely on probabilistic methods, social interaction and information theory, rather than on formal models of grammar induction. This book addresses the learning procedures through which humans acquire natural language, and the way in which they represent its properties. It brings together leading researchers from computational linguistics, psychology, behavioral science and mathematical linguistics to consider the significance of non-algebraic methods for the study of natural language. The text represents a wide spectrum of views, from the claim that algebraic systems are largely irrelevant to the contrary position that non-algebraic learning methods are engineering devices for efficiently identifying the patterns that underlying grammars and semantic models generate for natural language input. There are interesting and important perspectives that fall at intermediate points between these opposing approaches, and they may combine elements of both. It will appeal to researchers and advanced students in each of these fields, as well as to anyone who wants to learn more about the relationship between computational models and natural language.

This book constitutes the refereed proceedings of the 43rd German Conference on Artificial Intelligence, KI 2020, held in Bamberg, Germany, in September 2020. The 16 full and 12 short papers presented together with 6 extended abstracts in this volume were carefully reviewed and selected from 62 submissions. As well-established annual conference series KI is dedicated to research on theory and applications across all methods and topic areas of AI research. KI 2020 had a special focus on human-centered AI with highlights on AI and education and explainable machine learning. Due to the Corona pandemic KI 2020 was held as a virtual event.