The first detailed study of this most important class of systems which contain internal predictive models of themselves and/or of their environments and whose predictions are utilized for purposes of present control. This book develops the basic concept of a predictive model, and shows how it can be embedded into a system of feedforward control. Includes many examples and stresses analogies between wired-in anticipatory control and processes of learning and adaption, at both individual and social levels. Shows how the basic theory of such systems throws a new light both on analytic problems (understanding what is going on in an organism or a social system) and synthetic ones (developing forecasting methods for making individual or collective decisions).

The scope of these proceedings is the study, research, and development in the new frontier of science dealing with the paradigm of computing anticipatory systems. A computing anticipatory system is a system which computes its current states in taking into account its anticipatory states. Strong anticipation refers to an anticipation of events built by or embedded in a system. Weak anticipation refers to an anticipation of events predicted or forecast from a model of a system. These proceedings include all the papers by invited speakers who received awards at CASYS'03 as well as a selection of invited papers. Topics include: relativity, quantum mechanics and biophysical systems; anticipatory systems, adaptation and hyperincursion; mathematical models, chaos and soft computing; logic, algorithms and computing systems; living systems, evolution and cognitive models; decision support, enterprise and management models and control systems, simulation and chaos.

These proceedings deal with a selection of papers presented at the 8th International Conference CASYS’07, on COMPUTING ANTICIPATORY SYSTEMS, 6-11 August 2007, held at HEC Management School - University of Liege, Liège, Belgium. The content of these proceedings deals with the most recent Research & Development in the area of theoretical developments and applications in the modelling and computing of anticipation in any fields of natural and artificial systems. A computing anticipatory system is a system that computes its current states in taking into account its past and present states but also its potential future states. Strong anticipation refers to an anticipation of events built by or embedded in a system. Weak anticipation refers to an anticipation of events predicted or forecasted from a model of a system. This volume contains the first invited lecture “Neural Approach to Machine Consciousness”, by Prof. Dr Igor Aleksander, FREng (UK) who received the CHAOS AWARD, and the second invited lecture “Quantum Mechanics, Pattern Recognition, and the Mammalian Brain”, by Dr George Chapline (USA) who received the CASYS AWARD. The peer reviewed papers that received a BEST PAPER AWARD at CASYS’07, and some invited papers are included in these proceedings. Adel F. Antippa (Canada) and Daniel M. Dubois (Belgium) presented the continuation of their original research project on discrete physics with their third fundamental paper “Synchronous Discrete Harmonic Oscillator” that also appears in these proceedings. The interdisciplinary outstanding topics of these proceedings are readable by researchers and scientists with a university background.

The scope of these proceedings is the study, research, and development in the new frontier of science dealing with the paradigm of computing anticipatory systems. A computing anticipatory system is a system which computes its current states in taking into account its anticipatory states. Strong anticipation refers to an anticipation of events built by or embedded in a system. Weak anticipation refers to an anticipation of events predicted or forecast from a model of a system. These proceedings include all the papers by invited speakers who received awards at CASYS'03 as well as a selection of invited papers. Topics include: relativity, quantum mechanics and biophysical systems; anticipatory systems, adaptation and hyperincursion; mathematical models, chaos and soft computing; logic, algorithms and computing systems; living systems, evolution and cognitive models; decision support, enterprise and management models and control systems, simulation and chaos.

Robert Rosen was not only a biologist, he was also a brilliant mathematician whose extraordinary contributions to theoretical biology were tremendous. Founding, with this book, the area of Anticipatory Systems Theory is a remarkable outcome of his work in theoretical biology. This second edition of his book Anticipatory Systems, has been carefully revised and edited, and includes an Introduction by Judith Rosen. It has also been expanded with a set of Prolegomena by Dr. Mihai Nadin, who offers an historical survey of this fast growing field since the original work was published. There is also some exciting new work, in the form of an additional chapter on the Ontology of Anticipation, by Dr. John Kineman. An addendum-- with autobiographical reminiscences by Robert Rosen, himself, and a short story by Judith Rosen about her father-- adds a personal touch. This work, now available again, serves as the guiding foundations for the growing field of Anticipatory Systems and, indeed, any area of science that deals with living organisms in some way, including the study of Life and Mind. It will also be of interest to graduate students and researchers in the field of Systems Science.

Anticipatory Learning Classifier Systems describes the state of the art of anticipatory learning classifier systems-adaptive rule learning systems that autonomously build anticipatory environmental models. An anticipatory model specifies all possible action-effects in an environment with respect to given situations. It can be used to simulate anticipatory adaptive behavior. Anticipatory Learning Classifier Systems highlights how anticipations influence cognitive systems and illustrates the use of anticipations for (1) faster reactivity, (2) adaptive behavior beyond reinforcement learning, (3) attentional mechanisms, (4) simulation of other agents and (5) the implementation of a motivational module. The book focuses on a particular evolutionary model learning mechanism, a combination of a directed specializing mechanism and a genetic generalizing mechanism. Experiments show that anticipatory adaptive behavior can be simulated by exploiting the evolving anticipatory model for even faster model learning, planning applications, and adaptive behavior beyond reinforcement learning. Anticipatory Learning Classifier Systems gives a detailed algorithmic description as well as a program documentation of a C++ implementation of the system.

These AIP Conference Proceedings contain the papers of the two invited speakers: "Systems with Emergent Dynamics" by Ian Stewart (UK), who received the CHAOS AWARD, and "The Role of Anticipation in Intelligent Systems" by George J. Klir (USA), who received the CASYS'01 AWARD. Second, all the papers of the authors who received a Best Paper Award, and, third, a selection of invited papers. The scope is the study, research, and development in the new frontier of science dealing with the paradigm of computing anticipatory systems. A computing anticipatory system is a system which computes its current states in taking into account its anticipatory states. Strong anticipation refers to an anticipation of events built by or embedded in a system. Weak anticipation refers to an anticipation of events predicted or forecast from a model of a system. Topics include Anticipatory Systems, Cybernetics and Epistemology; Mathematical System, Chaos, Anticipation and Incursion; Relativity, Quantum Physics and Quantum Computing; Intelligent Agents, Learning and Cognitive Systems; Organisation, Regulation, Management and Planning; Control Systems, Robots, Neural Nets and Agents; and Information Science Models and Anticipatory Programs.

The interdisciplinary topic of anticipation, attracting attention fromnbsp;computer scientists, psychologists, philosophers, neuroscientists, and biologists is a rather new and often misunderstood matter of research. This book attempts to establish anticipation as a research topic and encourage further research and development work. First, the book presents philosophical thoughts and concepts to stimulate the reader's concern about the topic. Fundamental cognitive psychology experiments then confirm the existence of anticipatory behavior in animals and humans and outline a first framework of anticipatory learning and behavior. Next, several distinctions and frameworks of anticipatory processes are discussed, including first implementations of these concepts. Finally, several anticipatory systems and studies on anticipatory behavior are presented.

The availability of today’s online information systems rapidly increases the relevance of dynamic decision making within a large number of operational contexts. Whenever a sequence of interdependent decisions occurs, making a single decision raises the need for anticipation of its future impact on the entire decision process. Anticipatory support is needed for a broad variety of dynamic and stochastic decision problems from different operational contexts such as finance, energy management, manufacturing and transportation. Example problems include asset allocation, feed-in of electricity produced by wind power as well as scheduling and routing. All these problems entail a sequence of decisions contributing to an overall goal and taking place in the course of a certain period of time. Each of the decisions is derived by solution of an optimization problem. As a consequence a stochastic and dynamic decision problem resolves into a series of optimization problems to be formulated and solved by anticipation of the remaining decision process. However, actually solving a dynamic decision problem by means of approximate dynamic programming still is a major scientific challenge. Most of the work done so far is devoted to problems allowing for formulation of the underlying optimization problems as linear programs. Problem domains like scheduling and routing, where linear programming typically does not produce a significant benefit for problem solving, have not been considered so far. Therefore, the industry demand for dynamic scheduling and routing is still predominantly satisfied by purely heuristic approaches to anticipatory decision making. Although this may work well for certain dynamic decision problems, these approaches lack transferability of findings to other, related problems. This book has serves two major purposes: ‐ It provides a comprehensive and unique view of anticipatory optimization for dynamic decision making. It fully integrates Markov decision processes, dynamic programming, data mining and optimization and introduces a new perspective on approximate dynamic programming. Moreover, the book identifies different degrees of anticipation, enabling an assessment of specific approaches to dynamic decision making. ‐ It shows for the first time how to successfully solve a dynamic vehicle routing problem by approximate dynamic programming. It elaborates on every building block required for this kind of approach to dynamic vehicle routing. Thereby the book has a pioneering character and is intended to provide a footing for the dynamic vehicle routing community.

Rowlands offers researchers in quantum, theoretical and high energy physics immediate access to simple but powerful techniques.