In this book, the authors focus on three aspects related to the development of articulated agents: presenting an overview of high-level control algorithms for intelligent decision-making of articulated agents, experimental study of the properties of soft agents as the end-effector of articulated agents, and accurate management of low-level torque-control loop to accurately control the articulated agents. This book summarizes recent advances related to articulated agents. The motive behind the book is to trigger theoretical and practical research studies related to articulated agents.

Intelligent Decision-Making Support Systems (i-DMSS) are specialized IT-based systems that support some or several phases of the individual, team, organizational or inter-organizational decision making process by deploying some or several intelligent mechanisms. This book pursues the following academic aims: (i) generate a compendium of quality theoretical and applied contributions in Intelligent Decision-Making Support Systems (i-DMSS) for engineering and management IT-based service systems (ITSS); (ii) diffuse scarce knowledge about foundations, architectures and effective and efficient methods and strategies for successfully planning, designing, building, operating, and evaluating i-DMSS for ITSS, and (iii) create an awareness of, and a bridge between ITSS and i-DMSS academicians and practitioners in the current complex and dynamic engineering and management ITSS organizational. The book presents a collection of 11 chapters referring to relevant topics for both IT service systems and i-DMSS including: problems of selection of IT service providers, optimization of supply chain systems, IT governance decisions, clinical decision support, dynamic user-interface adaptation, re-engineering of processes, and generic decision problems. Advanced IT technologies used in some chapters are: fuzzy multi-criteria mechanisms, semantic processing, data mining processing, and rough sets. Other chapters report traditional DSS mechanisms but used or suggested to be used in innovative mode for IT service engineering and management tasks.

This book will be bought by researchers and graduates students in Artificial Intelligence and management as well as practising managers and consultants interested in the application of IT and information systems in real business environment.

While there are many different models for performing system analysis, the multi-criteria decision making method has proven to be one of the most efficient. By analyzing the key concepts of this theory, the technique can be enhanced and will benefit future organizations and companies in novel ways. Multi-Criteria Decision Making for the Management of Complex Systems provides a comprehensive examination of the latest strategies and methods involved in decision theory. Featuring extensive coverage on relevant topics such as nested scalar convolutions, Pareto optimality, nonlinear schemes, and operator performance, this publication is ideally designed for engineers, students, professionals, academics, and researchers seeking innovative perspectives on the supervision of advanced decision making theories in system analysis.

The study of complex systems attracts the attention of many researchers in diverse fields. Complex systems are characterized by a high number of entities and a high degree of interactions. One of the most important features is that they do not involve a central organizing authority, but the various elements that make up the systems are self-organized. Moreover, some complex systems possess an emergency priority: climate change and sustainable development research, studies of public health, ecosystem habitats, epidemiology, and medicine, among others. Unfortunately, a great number of today’s overlapping approaches fail to meet the needs of decision makers when managing complex domains. Indeed, the design of complex systems often requires the integration of a number of artificial intelligence tools and techniques. The problem can be viewed in terms of goals, states, and actions, choosing the best action to move the system toward its desired state or behavior. This is why agent-based approaches are used to model complex systems. The main objective of this book is to bring together existing methods for decision support systems creation within a coherent agent-based framework and to provide an interdisciplinary and flexible methodology for modeling complex and systemic domains.

Faced with ever-increasing complexity on a daily basis, the decision-makers of today are struggling to find the appropriate models, methods and tools to face the issues arising in complex systems across all levels of global operations. Having, in the past, resorted to outdated approaches which limit problem-solving to linear world views, we must now capitalize on complexities in order to succeed and progress in our society. This book provides a guide to harnessing the wealth inherent to complex systems. It organizes the transition to complex decision-making in all business spheres while providing many examples in various application domains. The authors offer fresh developments for understanding and mastering the global “uberization” of the economy, the post-modern management of computer-assisted production and the rise of cognitive robotics science applications.

In order to ensure the criteria for monitoring and managing the various problems and design for decision control, a mathematical description of exact human knowledge is required for the management of adaptive and complex systems. Decision Control, Management, and Support in Adaptive and Complex Systems: Quantitative Models presents an application and demonstration of a new mathematical technique for descriptions of complex systems. This comprehensive collection contains scientific results in the field of contemporary approaches to adaptive decision making that is essential for researchers, scholars, and students alike.