Safety has been ranked as the number one concern for the acceptance and adoption of automated vehicles since safety has driven some of the most complex requirements in the development of self-driving vehicles. Recent fatal accidents involving self-driving vehicles have uncovered issues in the way some automated vehicle companies approach the design, testing, verification, and validation of their products. Traditionally, automotive safety follows functional safety concepts as detailed in the standard ISO 26262. However, automated driving safety goes beyond this standard and includes other safety concepts such as safety of the intended functionality (SOTIF) and multi-agent safety. Multi-Agent Safety addresses the concept of safety for self-driving vehicles through the inclusion of 10 recent and highly relevent SAE technical papers. Topics that these papers feature include vehicle interaction with other vehicles, pedestrians, bicyclists, and other road objects. As the second title in a series on automated vehicle safety, each will contain introductory content by the Editor with 10 SAE technical papers specifically chosen to illuminate the specific safety topic of that book.

As intelligent autonomous agents and multiagent system applications become more pervasive, it becomes increasingly important to understand the risks associated with using these systems. Incorrect or inappropriate agent behavior can have harmful - fects, including financial cost, loss of data, and injury to humans or systems. For - ample, NASA has proposed missions where multiagent systems, working in space or on other planets, will need to do their own reasoning about safety issues that concern not only themselves but also that of their mission. Likewise, industry is interested in agent systems that can search for new supply opportunities and engage in (semi-) automated negotiations over new supply contracts. These systems should be able to securely negotiate such arrangements and decide which credentials can be requested and which credentials may be disclosed. Such systems may encounter environments that are only partially understood and where they must learn for themselves which aspects of their environment are safe and which are dangerous. Thus, security and safety are two central issues when developing and deploying such systems. We refer to a multiagent system’s security as the ability of the system to deal with threats that are intentionally caused by other intelligent agents and/or s- tems, and the system’s safety as its ability to deal with any other threats to its goals.

Safety has been ranked as the number one concern for the acceptance and adoption of automated vehicles since safety has driven some of the most complex requirements in the development of self-driving vehicles. Recent fatal accidents involving self-driving vehicles have uncovered issues in the way some automated vehicle companies approach the design, testing, verification, and validation of their products. Traditionally, automotive safety follows functional safety concepts as detailed in the standard ISO 26262. However, automated driving safety goes beyond this standard and includes other safety concepts such as safety of the intended functionality (SOTIF) and multi-agent safety. Multi-Agent Safety addresses the concept of safety for self-driving vehicles through the inclusion of 10 recent and highly relevent SAE technical papers. Topics that these papers feature include vehicle interaction with other vehicles, pedestrians, bicyclists, and other road objects. As the second title in a series on automated vehicle safety, each will contain introductory content by the Editor with 10 SAE technical papers specifically chosen to illuminate the specific safety topic of that book.

Multi-agent intelligence in autonomous systems has been fascinating roboticists for decades. The recent advances in machine learning has created unprecedented opportunities for achieving ultimate multi-agent intelligence and full autonomy in a data-driven way. However, a fundamental bottleneck of machine learning-based methods is their safety and reliability in controlling the autonomous system at large scale, due to the lack of formal safety guarantee. In addressing these challenges, we develop: (1) An machine learning-based large-scale multi-agent control framework with safety certificates, which simultaneously enjoys the versatility of machine learning and the assurance of safety. (2) A multi-agent trajectory tracking framework with convergence and safety guarantees. (3) A general method to learn safe controllers for black-box systems with unknown dynamics. Comprehensive experiments have shown that the proposed methods have notable performance in terms of safety rate, task completion rate, computational efficiency and large-scale scalability.

This volume gathers the peer reviewed papers presented at the 4th edition of the International Workshop “Service Orientation in Holonic and Multi-agent Manufacturing – SOHOMA’14” organized and hosted on November 5-6, 2014 by the University of Lorraine, France in collaboration with the CIMR Research Centre of the University Politehnica of Bucharest and the TEMPO Laboratory of the University of Valenciennes and Hainaut-Cambrésis. The book is structured in six parts, each one covering a specific research line which represents a trend in future manufacturing: (1) Holonic and Agent-based Industrial Automation Systems; (2) Service-oriented Management and Control of Manufacturing Systems; (3) Distributed Modelling for Safety and Security in Industrial Systems; (4) Complexity, Big Data and Virtualization in Computing-oriented Manufacturing; (5) Adaptive, Bio-inspired and Self-organizing Multi-Agent Systems for Manufacturing and (6) Physical Internet Simulation, Modelling and Control. There is a clear orientation of the SOHOMA’14 workshop towards complexity, which is a common view of all six parts. There is need for a framework allowing the development of manufacturing cyber physical systems including capabilities for complex event processing and data analytics which are expected to move the manufacturing domain closer towards cloud manufacturing within contextual enterprises. Recent advances in sensor, communication and intelligent computing technologies made possible the Internet connectivity of the physical world: the Physical Internet, where not only documents and images are created, shared, or modified in the cyberspace, but also the physical resources and products interact over Internet and make decisions based on shared communication.

This volume constitutes the refereed proceedings of the Third International Conference on Industrial Applications of Holonic and Multi-Agent Systems held in September 2007. The 39 full papers were selected from among 63 submissions. They are organized into topical sections covering theoretical and methodological issues, algorithms and technologies, implementation and validation, applications, and supply chain management.