This book provides detailed descriptions of big data solutions for activity detection and forecasting of very large numbers of moving entities spread across large geographical areas. It presents state-of-the-art methods for processing, managing, detecting and predicting trajectories and important events related to moving entities, together with advanced visual analytics methods, over multiple heterogeneous, voluminous, fluctuating and noisy data streams from moving entities, correlating them with data from archived data sources expressing e.g. entities' characteristics, geographical information, mobility patterns, mobility regulations and intentional data. The book is divided into six parts: Part I discusses the motivation and background of mobility forecasting supported by trajectory-oriented analytics, and includes specific problems and challenges in the aviation (air-traffic management) and the maritime domains. Part II focuses on big data quality assessment and processing, and presents novel technologies suitable for mobility analytics components. Next, Part III describes solutions toward processing and managing big spatio-temporal data, particularly enriching data streams and integrating streamed and archival data to provide coherent views of mobility, and storing of integrated mobility data in large distributed knowledge graphs for efficient query-answering. Part IV focuses on mobility analytics methods exploiting (online) processed, synopsized and enriched data streams as well as (offline) integrated, archived mobility data, and highlights future location and trajectory prediction methods, distinguishing between short-term and more challenging long-term predictions. Part V examines how methods addressing data management, data processing and mobility analytics are integrated in big data architectures with distinctive characteristics compared to other known big data paradigmatic architectures. Lastly, Part VI covers important ethical issues that research on mobility analytics should address. Providing novel approaches and methodologies related to mobility detection and forecasting needs based on big data exploration, processing, storage, and analysis, this book will appeal to computer scientists and stakeholders in various application domains.

This book is dedicated to Marek Sergot, Professor in Computational Logic at Imperial College London, on the occasion of his 60th birthday. Professor Sergot's scientific contributions range over many different fields. He has developed a series of novel ideas and formal methods bridging areas including artificial intelligence, computational logic, philosophical logic, legal theory, artificial intelligence and law, multi-agent systems and bioinformatics. By combining his background in logic and computing with his interest in the law, deontic logic, action, and related areas, and applying to all his capacity to understand the subtleties of social interaction and normative reasoning, Professor Sergot has opened up new directions of research, and has been a reference, an inspiration, and a model for many researchers in the fields to which he has contributed. The Festschrift includes several reminiscences and introductory essays describing Professor Sergot's achievements, followed by a series of articles on logic programming, temporal reasoning and action languages, artificial intelligence and law, deontic logic and norm-governed systems, and logical approaches to policies.

Adaptation, for purposes of self-healing, self-protection, self-management, or self-regulation, is currently considered to be one of the most challenging pr- erties of distributed systems that operate in dynamic, unpredictable, and - tentially hostile environments. Engineering for adaptation is particularly c- plicated when the distributed system itself is composed of autonomous entities that, on one hand, may act collaboratively and with benevolence, and, on the other, maybehavesel?shlywhilepursuingtheirowninterests.Still, theseentities have to coordinate themselves in order to adapt appropriately to the prevailing environmental conditions, and furthermore, to deliberate upon their own and the system's con?guration, and to be transparent to their users yet consistent with any human requirements. The question, therefore, of "how to organize the envisagedadaptationforsuchautonomousentitiesinasystematicway"becomes of paramount importance. The ?rst international workshop on "Organized Adaptation in Multi-Agent Systems" (OAMAS) was a one-day event held as part of the workshop p- gram arranged by the international conference on Autonomous Agents and Multi-Agent Systems (AAMAS). It was hosted in Estoril during May, 2008, and was attended by more than 30 researchers. OAMAS was the steady convergence of a number of lines of research which suggested that such a workshop would be timely and opportune. This includes the areas of autonomic computing, swarm intelligence, agent societies, self-organizing complex systems, and 'emergence' in general.

This book constitutes the thoroughly refereed post-conference proceedings of the 9th International Workshop on Engineering Societies in the Agents World, ESAW 2008, held in Saint-Etienne, France, in September 2008.

The 13 revised full papers presented together with 1 invited long paper were carefully selected from 29 submissions during two rounds of reviewing and revision. The papers are organized in topical sections on organisations and norm-governed systems, privacy and security, agent-oriented software engineering, emergence and self-organisation, as well as simulation.

The 8th annual international workshop "Engineering Societies in the Agents' World" was hosted by the National Centre for Scienti?c Research "Demok- tos," in Athens, Greece, in October 2007. The workshop was organized as a stand-alone event, running over three days. ESAW 2007 built upon the success of prior ESAW workshops: ESAW 2006 held in Dublin, ESAW 2005 held in Ku, sadasi, going back to the ?rst ESAW workshop, which was held in Berlin in 2000. ESAW 2007 was attended by 40 participants from 10 di?erent countries. Eachpresentationwasfollowedby highly interactivediscussions, in line with the ESAW spirit of having open discussions with fellow experts. The ESAW workshop series started in 2000 to provide a forum for prese- ing highly inter-disciplinary workon technologies, methodologies, platforms and tools for the engineering of complex arti?cial agent societies. Such systems have found applications in many diverse domains such as space ?ight operations, e-business and ambient intelligence. Despite ESAW traditionally placing - phasis on practical engineering issues and applications, the workshop did not exclude theoretical and philosophical contributions, on the proviso that they clearly documented their connection to the core applied issues. Discussions coalesced around the following themes: - electronic institutions; - models of complex distributed systems with agents and societies; - interaction in agent societies; - engineering social intelligence in multi-agent systems; - trust and reputation in agent societies; - analysis, design and development of agent societies."

This book constitutes the refereed conference proceedings of the 30th International Conference on Inductive Logic Programming, ILP 2021, held in October 2021. Due to COVID-19 pandemic the conference was held virtually. The 16 papers and 3 short papers presented were carefully reviewed and selected from 19 submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.

In the last 25 years, information systems have had a disruptive effect on society and business. Up until recently though, the majority of passengers and goods were transported by sea in many ways similar to the way they were at the turn of the previous century. Gradually, advanced information technologies are being introduced, in an attempt to make shipping safer, greener, more efficient, and transparent. The emerging field of Maritime Informatics studies the application of information technology and information systems to maritime transportation. Maritime Informatics can be considered as both a field of study and domain of application. As an application domain, it is the outlet of innovations originating from data science and artificial intelligence; as a field of study, it is positioned between computer science and marine engineering. This new field's complexity lies within this duality because it is faced with disciplinary barriers yet demands a systemic, transdisciplinary approach. At present, there is a growing body of knowledge that remains undocumented in a single source or textbook designed to assist students and practitioners. This highly useful textbook/reference starts by introducing required knowledge, algorithmic approaches, and technical details, before presenting real-world applications. The aim is to present interested audiences with an overview of the main technological innovations having a disruptive effect on the maritime industry, as well as to discuss principal ideas, methods of operation and applications, and future developments. The material in this unique volume provides requisite core knowledge for undergraduate or postgraduate students, employing an analytical approach with numerous real-world examples and case studies.

This book provides detailed descriptions of big data solutions for activity detection and forecasting of very large numbers of moving entities spread across large geographical areas. It presents state-of-the-art methods for processing, managing, detecting and predicting trajectories and important events related to moving entities, together with advanced visual analytics methods, over multiple heterogeneous, voluminous, fluctuating and noisy data streams from moving entities, correlating them with data from archived data sources expressing e.g. entities' characteristics, geographical information, mobility patterns, mobility regulations and intentional data. The book is divided into six parts: Part I discusses the motivation and background of mobility forecasting supported by trajectory-oriented analytics, and includes specific problems and challenges in the aviation (air-traffic management) and the maritime domains. Part II focuses on big data quality assessment and processing, and presents novel technologies suitable for mobility analytics components. Next, Part III describes solutions toward processing and managing big spatio-temporal data, particularly enriching data streams and integrating streamed and archival data to provide coherent views of mobility, and storing of integrated mobility data in large distributed knowledge graphs for efficient query-answering. Part IV focuses on mobility analytics methods exploiting (online) processed, synopsized and enriched data streams as well as (offline) integrated, archived mobility data, and highlights future location and trajectory prediction methods, distinguishing between short-term and more challenging long-term predictions. Part V examines how methods addressing data management, data processing and mobility analytics are integrated in big data architectures with distinctive characteristics compared to other known big data paradigmatic architectures. Lastly, Part VI covers important ethical issues that research on mobility analytics should address. Providing novel approaches and methodologies related to mobility detection and forecasting needs based on big data exploration, processing, storage, and analysis, this book will appeal to computer scientists and stakeholders in various application domains.