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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 whyagent-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.
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 whyagent-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.