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This book looks at the process of human cognition and the way complex problems are solved by decomposing them into a list of strategic objectives, before focusing individually on each objective to plan for a tactical solution. This process has been formulated by military planners in the form of the Standard Operating Procedure, by which problem solving is organised into four different stages: deliberation, planning, war meeting and plan execution. This has enabled the development of a methodology for problem solving in a dynamic environment. This is illustrated with the help of a six-case study in chess and prediction of exchange rate movement in a foreign exchange market.
This book provides basic management knowledge in a clearly structured way. Fundamental aspects of management are described, on the basis of which a model of the enterprise is outlined. This allows readers to find their way around easily, to reflect, then to set new approaches in context and examine them in a critical light. The practical examples, the interpretation questions, and the short case studies at the end of the chapters facilitate the transition from theory to practice.
Based on a broad range of case studies, Organization and Management Problem Solving is an insightful text designed to improve the application of organization theory and systems thinking in teaching and practice. This book illustrates the five key themes in the nature of organization and managementa'technical, structural, psychosocial, managerial, and culturala'through the analysis of measured incidents tested by students. A clear theoretical framework supports the case studies, allowing the text to have practical relevance to contemporary settings and to be recognized as a model for describing, analyzing, and responding to organization and management problems. The model integrates the thinking of many writers on organization and problem solving including Ackoff, Blake, and Mouton; Schein, Kast, and Rosenweign; and Mitroff and Lippitt. The approach eliminates causal conditions and emphasizes responsive problem solving. Theory is applied and expanded as needed to a broader social context, engaging the reader in a thorough understanding of the nature and development of organization theory and problem solving. This book is relevant to consultants, academics, and professional managers in a number of settings (academic, military, business organizations, and research institutes) and disciplines (including development and change, management, human resources, social psychology, communication, sociology, and psychology).
Solving non-routine problems is a key competence in a world full of changes, uncertainty and surprise where we strive to achieve so many ambitious goals. But the world is also full of solutions because of the extraordinary competences of humans who search for and find them.
Autonomous agents or multiagent systems are computational systems in which several computational agents interact or work together to perform some set of tasks. These systems may involve computational agents having common goals or distinct goals. Real-Time Search for Learning Autonomous Agents focuses on extending real-time search algorithms for autonomous agents and for a multiagent world. Although real-time search provides an attractive framework for resource-bounded problem solving, the behavior of the problem solver is not rational enough for autonomous agents. The problem solver always keeps the record of its moves and the problem solver cannot utilize and improve previous experiments. Other problems are that although the algorithms interleave planning and execution, they cannot be directly applied to a multiagent world. The problem solver cannot adapt to the dynamically changing goals and the problem solver cannot cooperatively solve problems with other problem solvers. This book deals with all these issues. Real-Time Search for Learning Autonomous Agents serves as an excellent resource for researchers and engineers interested in both practical references and some theoretical basis for agent/multiagent systems. The book can also be used as a text for advanced courses on the subject.
This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems. The motivation for this book arises from the fact that many real-world optimization problems and engineering systems are subject to dynamic environments, where changes occur over time. Key issues for addressing dynamic optimization problems in evolutionary computation, including fundamentals, algorithm design, theoretical analysis, and real-world applications, are presented. "Evolutionary Computation for Dynamic Optimization Problems" is a valuable reference to scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, nature- and bio-inspired computing, and evolutionary computation.
This text examines how multiobjective evolutionary algorithms and related techniques can be used to solve problems, particularly in the disciplines of science and engineering. Contributions by leading researchers show how the concept of multiobjective optimization can be used to reformulate and resolve problems in areas such as constrained optimization, co-evolution, classification, inverse modeling, and design.
This book compiles recent advances of evolutionary algorithms in dynamic and uncertain environments within a unified framework. The book is motivated by the fact that some degree of uncertainty is inevitable in characterizing any realistic engineering systems. Discussion includes representative methods for addressing major sources of uncertainties in evolutionary computation, including handle of noisy fitness functions, use of approximate fitness functions, search for robust solutions, and tracking moving optimums.
This book constitutes the refereed proceedings of the International Conference on the Applications of Evolutionary Computation, EvoApplications 2011, held in Torino, Italy, in April 2011 colocated with the Evo* 2011 events. Thanks to the large number of submissions received, the proceedings for EvoApplications 2011 are divided across two volumes (LNCS 6624 and 6625). The present volume contains contributions for EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC. The 36 revised full papers presented were carefully reviewed and selected from numerous submissions. This volume presents an overview about the latest research in EC. Areas where evolutionary computation techniques have been applied range from telecommunication networks to complex systems, finance and economics, games, image analysis, evolutionary music and art, parameter optimization, scheduling, and logistics. These papers may provide guidelines to help new researchers tackling their own problem using EC.
Contents:How Many "Demons" Do We Need? Endophysical Self-Creation of Material Structures and the Exophysical Mystery of Universal Libraries (G Kampis & O E Rössler)Some Implications of Re-Interpretation of the Turing Test for Cognitive Science and Artificial Intelligence (G Werner)Why Economic Forecasts will be Overtaken by the Facts (J D M Kruisinga)Simulation Methods in Peace and Conflict Research (F Breitenecker et al)Software Development Paradigms: A Unifying Concept (G Chroust)Hybrid Hierarchies: A Love-Hate Relationship Between ISA and SUPERC (D Castelfranchi & D D'Aloisi)AI for Social Citizenship: Towards an Anthropocentric Technology (K S Gill)Organizational Cybernetics and Large Scale Social Reforms in the Context of Ongoing Developments (E Bekjarov & A Athanassov)China's Economic Reform and its Obstacles: Challenges to a Large-Scale Social Experiment (J Hu & X Sun)Comparing Conceptual Systems: A Strategy for Changing Values as well as Institutions (S A Umpleby)and others Readership: Researchers in the fields of cybernetics and systems, artificial intelligence, economics and mathematicians.