Bøger af Danny Weyns

Contains the refereed post-conference proceedings of the First International Self-Organizing Architectures Workshop (SOAR) in Cambridge, UK, in September 2009. This book includes 9 revised papers, which were selected from 17 submissions of the workshop, as well as 4 invited papers.

A concise and practical introduction to the foundations and engineering principles of self-adaptationThough it has recently gained significant momentum, the topic of self-adaptation remains largely under-addressed in academic and technical literature. This book changes that. Using a systematic and holistic approach, An Introduction to Self-adaptive Systems: A Contemporary Software Engineering Perspective provides readers with an accessible set of basic principles, engineering foundations, and applications of self-adaptation in software-intensive systems.It places self-adaptation in the context of techniques like uncertainty management, feedback control, online reasoning, and machine learning while acknowledging the growing consensus in the software engineering community that self-adaptation will be a crucial enabling feature in tackling the challenges of new, emerging, and future systems.The author combines cutting-edge technical research with basic principles and real-world insights to create a practical and strategically effective guide to self-adaptation. He includes features such as:* An analysis of the foundational engineering principles and applications of self-adaptation in different domains, including the Internet-of-Things, cloud computing, and cyber-physical systems* End-of-chapter exercises at four different levels of complexity and difficulty* An accompanying author-hosted website with slides, selected exercises and solutions, models, and codePerfect for researchers, students, teachers, industry leaders, and practitioners in fields that directly or peripherally involve software engineering, as well as those in academia involved in a class on self-adaptivity, this book belongs on the shelves of anyone with an interest in the future of software and its engineering.

Software intensive systems are increasingly expected to deal with changing user needs and dynamic operating conditions at run time. Examples are the need for life recon?gurations, management of resource variability, and dealing with p- ticular failure modes. Endowing systems with these kinds of capabilities poses severe challenges to software engineers and necessitates the development of new techniques, practices, and tools that build upon sound engineering principles. The ?eld of multi-agent systems focuses on the foundations and engineering of systems that consists of a network of autonomous entities (agents) that int- act to achieve the system goals. One line of research in multi-agent systems, inspired by biological, physical and other naturally occurring systems, concerns multi-agent systems in which agents share information and coordinate their - havior througha shared medium called an agentenvironment. Typical examples are gradient ?elds and digital pheromones that guide agents in their local c- text and as such facilitate the coordination of a community of agents. Since environment-mediation in multi-agent systems has shown to result in mana- able solutions with very adaptable qualities, it is a promising paradigm to deal with the increasing complexity and dynamism of distributed applications. Control in environment-mediated multi-agent systems is decentralized, i. e. , noneofthecomponentshasfullaccessorcontroloverthesystem. Self-organization isanapproachtoengineerdecentralized,distributedandresource-limitedsystems thatarecapableofdynamicallyadaptingtochangingconditionsandrequirements without external intervention. This useful system property is often re?ected in functionssuchasself-con?guration,self-optimization,andself-healing. Engine- ing approaches to self-organizing systems often rely on global functionality to emerge from localand autonomous decisions of individual agents that commu- catethroughasharedagentenvironment.

The modern ?eld of multiagent systems has developed from two main lines of earlier research. Its practitioners generally regard it as a form of arti?cial intelligence (AI). Some of its earliest work was reported in a series of workshops in the US dating from1980,revealinglyentitled,"e;DistributedArti?cialIntelligence,"e;andpioneers often quoted a statement attributed to Nils Nilsson that "e;all AI is distributed. "e; The locus of classical AI was what happens in the head of a single agent, and much MAS research re?ects this heritage with its emphasis on detailed modeling of the mental state and processes of individual agents. From this perspective, intelligenceisultimatelythepurviewofasinglemind,thoughitcanbeampli?ed by appropriate interactions with other minds. These interactions are typically mediated by structured protocols of various sorts, modeled on human conver- tional behavior. But the modern ?eld of MAS was not born of a single parent. A few - searchershavepersistentlyadvocatedideasfromthe?eldofarti?ciallife(ALife). These scientists were impressed by the complex adaptive behaviors of commu- ties of animals (often extremely simple animals, such as insects or even micro- ganisms). The computational models on which they drew were often created by biologists who used them not to solve practical engineering problems but to test their hypotheses about the mechanisms used by natural systems. In the ar- ?cial life model, intelligence need not reside in a single agent, but emerges at the level of the community from the nonlinear interactions among agents. - cause the individual agents are often subcognitive, their interactions cannot be modeled by protocols that presume linguistic competence.

This book puts the development of multi-agent systems into a larger perspective with traditional software engineering approaches to tackle difficult challenges of modern-day software systems, such as decentralized control, self-adaptation, and large-scale.