Service Intelligence and Service Science: Evolutionary Technologies and Challenges the emerging fields of service intelligence and service science, positioning them as the most promising directions for the evolution of service computing. This book demonstrates the critical role such areas play in supporting service computing processes, and furthers an increase in current research, best practices, and new directions in service computing technologies and applications.

This compendium covers several important topics related to multiagent systems, from learning and game theoretic analysis, to automated negotiation and human-agent interaction. Each chapter is written by experienced researchers working on a specific topic in mutliagent system interactions, and covers the state-of-the-art research results related to that topic.The book will be a good reference material for researchers and graduate students working in the area of artificial intelligence/machine learning, and an inspirational read for those in social science, behavioural economics and psychology.

This book contains the carefully selected and reviewed papers presented at three satellite events that were held in conjunction with the 11th International Conference on Web Information Systems Engineering, WISE 2010, in Hong Kong, China, in December 2010. The collection comprises a total of 40 contributions that originate from the First International Symposium on Web Intelligent Systems and Services (WISS 2010), from the First International Workshop on Cloud Information Systems Engineering (CISE 2010) and from the Second International Workshop on Mobile Business Collaboration (MBC 2010). The papers address a wide range of hot topics and are organized in topical sections on: decision and e-markets; rules and XML; web service intelligence; semantics and services; analyzing web resources; engineering web systems; intelligent web applications; web communities and personalization; cloud information system engineering; mobile business collaboration.

This book mainly aims at solving the problems in both cooperative and competitive multi-agent systems (MASs), exploring aspects such as how agents can effectively learn to achieve the shared optimal solution based on their local information and how they can learn to increase their individual utility by exploiting the weakness of their opponents. The book describes fundamental and advanced techniques of how multi-agent systems can be engineered towards the goal of ensuring fairness, social optimality, and individual rationality; a wide range of further relevant topics are also covered both theoretically and experimentally. The book will be beneficial to researchers in the fields of multi-agent systems, game theory and artificial intelligence in general, as well as practitioners developing practical multi-agent systems.