This text shows that the science of complex systems, which stresses the importance of self-organizing processes, can make a decisive contribution to solving many problems in artificial intelligence. Artificial cognitive systems are important in view of their potential applications, and it can be expected that their study will shed light on biological cognitive systems. The new "neurally inspired" information science proposed in this book is increasingly used as a workshop for the construction of models capable of emulating cognitive behaviour. After a general introduction to the theory of complex systems, the book gives a thorough treatment of neural networks, which are the most successful and the most thoroughly studied dynamical cognitive systems. Attention is also devoted to other classes of artificial cognitive systems, in particular to classifier systems, which provide an important link between the dynamical and the inferential approach to artificial intelligence. The book can be used as a textbook, since it does not require previous knowledge of the topic.
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This volume describes our intellectual path from the physics of complex sys tems to the science of artificial cognitive systems. It was exciting to discover that many of the concepts and methods which succeed in describing the self organizing phenomena of the physical world are relevant also for understand ing cognitive processes. Several nonlinear physicists have felt the fascination of such discovery in recent years. In this volume, we will limit our discussion to artificial cognitive systems, without attempting to model either the cognitive behaviour or the nervous structure of humans or animals. On the one hand, such artificial systems are important per se; on the other hand, it can be expected that their study will shed light on some general principles which are relevant also to biological cognitive systems. The main purpose of this volume is to show that nonlinear dynamical systems have several properties which make them particularly attractive for reaching some of the goals of artificial intelligence. The enthusiasm which was mentioned above must however be qualified by a critical consideration of the limitations of the dynamical systems approach. Understanding cognitive processes is a tremendous scientific challenge, and the achievements reached so far allow no single method to claim that it is the only valid one. In particular, the approach based upon nonlinear dynamical systems, which is our main topic, is still in an early stage of development.
This text shows that the science of complex systems, which stresses the importance of self-organizing processes, can make a decisive contribution to solving many problems in artificial intelligence. Artificial cognitive systems are important in view of their potential applications, and it can be expected that their study will shed light on biological cognitive systems. The new "neurally inspired" information science proposed in this book is increasingly used as a workshop for the construction of models capable of emulating cognitive behaviour. After a general introduction to the theory of complex systems, the book gives a thorough treatment of neural networks, which are the most successful and the most thoroughly studied dynamical cognitive systems. Attention is also devoted to other classes of artificial cognitive systems, in particular to classifier systems, which provide an important link between the dynamical and the inferential approach to artificial intelligence. The book can be used as a textbook, since it does not require previous knowledge of the topic.
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