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Distributed Sensor Networks
Distributed Sensor Networks is the first book of its kind to examine solutions to this problem using ideas taken from the field of multiagent systems. The field of multiagent systems has itself seen an exponential growth in the past decade, and has developed a variety of techniques for distributed resource allocation. Distributed Sensor Networks contains contributions from leading, international researchers describing a variety of approaches to this problem based on examples of implemented systems taken from a common distributed sensor network application; each approach is motivated, demonstrated and tested by way of a common challenge problem. The book focuses on both practical systems and their theoretical analysis, and is divided into three parts: the first part describes the common sensor network challenge problem; the second part explains the different technical approaches to the common challenge problem; and the third part provides results on the formal analysis of a number of approaches taken to address the challenge problem.
Extending Web Services Technologies
Extending Web Services Technologies addresses the rapidly growing impact of Multi-Agent Systems on web services tools and techniques. In particular, the book addresses the potential for MAS techniques to impact the difficult challenges that must be tackled for web services technology to realize its promises. The area of web services offers the multi-agent community exciting research possibilities, including similarities in system architectures, powerful tools, and a focus on issues such as trust and reliability. Likewise, techniques developed in the multi-agent research community promise to have a strong impact on this fast growing technology. The contents contain contributions by leading international researchers and professionals from both the web services and Multi-Agent Systems community. Topics include semantic web services and associated standards, architectures integrating agents and services, transactions, authorization, and service composition.
An Application Science for Multi-Agent Systems
An Application Science For Multi-Agent Systems addresses the complexity of choosing which multi-agent control technologies are appropriate for a given problem domain or a given application. Without such knowledge, when faced with a new application domain, agent developers must rely on past experience and intuition to determine whether a multi-agent system is the right approach, and if so, how to structure the agents, how to decompose the problem, and how to coordinate the activities of the agents, and so forth. This unique collection of contributions, written by leading international researchers in the agent community, provides valuable insight into the issues of deciding which technique to apply and when it is appropriate to use them. The contributions also discuss potential trade-offs or caveats involved with each decision. An Application Science For Multi-Agent Systems is an excellent reference for anyone involved in developing multi-agent systems.
Methodologies and Software Engineering for Agent Systems
As information technologies become increasingly distributed and accessible to larger number of people and as commercial and government organizations are challenged to scale their applications and services to larger market shares, while reducing costs, there is demand for software methodologies and appli- tions to provide the following features: Richer application end-to-end functionality; Reduction of human involvement in the design and deployment of the software; Flexibility of software behaviour; and Reuse and composition of existing software applications and systems in novel or adaptive ways. When designing new distributed software systems, the above broad requi- ments and their translation into implementations are typically addressed by partial complementarities and overlapping technologies and this situation gives rise to significant software engineering challenges. Some of the challenges that may arise are: determining the components that the distributed applications should contain, organizing the application components, and determining the assumptions that one needs to make in order to implement distributed scalable and flexible applications, etc.
Autonomy Oriented Computing
Autonomy Oriented Computing is a comprehensive reference for scientists, engineers, and other professionals concerned with this promising development in computer science. It can also be used as a text in graduate/undergraduate programs in a broad range of computer-related disciplines, including Robotics and Automation, Amorphous Computing, Image Processing, Programming Paradigms, Computational Biology, etc. Part One describes the basic concepts and characteristics of an AOC system and enumerates the critical design and engineering issues faced in AOC system development. Part Two gives detailed analyses of methodologies and case studies to evaluate AOC used in problem solving and complex system modeling. The final chapter outlines possibilities for future research and development. Numerous illustrative examples, experimental case studies, and exercises at the end of each chapter of Autonomy Oriented Computing help particularize and consolidate the methodologies and theories presented.
Communication in Multiagent Systems
Agents in multiagent systems are concurrent autonomous entities that need to coordinate and to cooperate so as to perform their tasks; these coordination and cooperation tasks might be achieved through communication. Communication, also called interaction by some authors, thus represents one of the major topics in multiagent systems. The state of the art of research on communication in multiagent systems is presented in this book. First, three seminal papers by Cohen and Perrault, by Singh, and by Davis and Smith present background information and introduce the newcomer to the area. The main part of the book is devoted to current research work dealing with agent communication, communication for coordination and argumentation, protocols, and dialogue games and conversational agents. Finally, the last paper deals with the future of agent communication.
Multi-Agent Programming
Multi-Agent Programming is an essential reference for anyone interested in the most up-to-date developments in MAS programming. While previous research has focused on the development of formal and informal approaches to analyze and specify Multi-Agent Systems, this book focuses on the development of programming languages and tools which not only support MAS programming, but also implement key concepts of MAS in a unified framework. Part I describes approaches that rely on computational logic or process algebra – Jason, 3APL, IMPACT, and CLAIM/SyMPA. Part II presents languages and platforms that extend or are based on Java – JADE, Jadex and JACKTM. Part III provides two significant industry specific applications – The DEFACTO System for coordinating human-agent teams for disaster response, and the ARTIMIS rational dialogue agent technology. Also featured are seven appendices for quick reference and comparison.
Agent Intelligence Through Data Mining
This book addresses the use of data mining for smarter, more efficient agents, as well as the challenge of generating intelligence from data while transferring it to a separate, possibly autonomous, software entity. Following a brief review of data mining and agent technology fields, the book presents a methodology for developing multi-agent systems, describes available open-source tools, and demonstrates the application of the methodology on three different cases.
Malware Analysis Using Artificial Intelligence and Deep Learning
This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging malware-related problems. DL and AI based approaches to malware detection and analysis are largely data driven and hence minimal expert domain knowledge of malware is needed. This book fills a gap between the emerging fields of DL/AI and malware analysis. It covers a broad range of modern and practical DL and AI techniques, including frameworks and development tools enabling the audience to innovate with cutting-edge research advancements in a multitude of malware (and closely related) use cases.
