High IQs don't improve the world. Adaptive intelligence does, because it prioritizes the common good over individual success.

Adaptive business intelligence systems combine prediction and optimization techniques to assist decision makers in complex, rapidly changing environments. These systems address fundamental questions: What is likely to happen in the future? What is the best course of action? Adaptive Business Intelligence explores elements of data mining, predictive modeling, forecasting, optimization, and adaptability. The book explains the application of numerous prediction and optimization techniques, and shows how these concepts can be used to develop adaptive systems. Coverage includes linear regression, time-series forecasting, decision trees and tables, artificial neural networks, genetic programming, fuzzy systems, genetic algorithms, simulated annealing, tabu search, ant systems, and agent-based modeling.

The "intelligence" of traditional artificial intelligence systems is notoriously narrow and inflexible--incapable of adapting to the constantly changing circumstances of the real world. Although traditional artificial intelligence systems can be successful in narrowly prescribed domains, they are inappropriate for dynamic, complex domains, such as autonomous robot navigation.**This book proposes an alternative methodology for designing intelligent systems based on a model of intelligence as adaptive behavior. The author describes an experiment in computational neuroethology--the computer modeling of neuronal control of behavior--in which the nervous system for an artificial insect is modeled. The experiment demonstrates that simple, complete intelligent agents are able to cope with complex, dynamic environments--suggesting that adaptive models of intelligence, based on biological bases of adaptive behavior, may prove to be very useful in the design of intelligent, autonomous systems

Argues people need 3 kinds of intelligence to be successful in life: analytical, creative and practical.

Theory of Adaptive Structures provides the basic theory for controlling adaptive structures in static and dynamic environments. It synthesizes well-established theories on modern control as well as statics and dynamics of deformable bodies. Discussions concentrate on the discrete parameter adaptive structures dealing with actuator placement, actuator selection, and actuation computation problems - keeping these structures at close proximity of any chosen nominal state with the least energy consumption. An introduction to the distributed parameter adaptive structures is also provided. The book follows that modern trend in research and industry striving to incorporate intelligence into engineered products through microprocessors that are becoming smaller, faster, and cheaper at astounding rates. Not using them in engineered products may become an enormous liability. Resulting from the advances in materials technology on sensors and actuator technologies as well as the availability of very powerful and reliable microprocessors, there is an ever-increasing interest in actively controlling the behavior of engineering systems. Engineers and engineering scientists must revive and broaden their activities to maximize applications for predicting and controlling the behavior of deformable bodies. Topics include: An introduction to adaptive structures Incremental excitation-response relations in static and dynamic cases Active control of response in static case Statically determinate adaptive structures Statically indeterminate adaptive structures Active vibration control for autonomous and non-autonomous cases Active control against wind Active control against seismic loads Distributed parameter adaptive structures The technology of adaptive structures has created an environment where the analysis, not the computation, of structural response - due to actuator-inserted deformations - has become important. Problems related to the placement, the operation in real time, and the energy consumption of the actuators require the review and broadening of the theories long dormant due to the emphasis placed in the numerical simulations of structural behavior by the displacement finite element method. This book furnishes the basic theory needed by modern engineers in the design and control of discrete parameter adaptive structures .

As ubiquitous as the atmosphere, intelligent adaptive systems (IASs) surround us in our daily lives. When designed well, these systems sense users and their environments so that they can provide support in a manner that is not only responsive to the evolving situation, but unnoticed by the user. A synthesis of recent research and developments on IASs from the human factors (HF) and human–computer interaction (HCI) domains, Intelligent Adaptive Systems: An Interaction-Centered Design Perspective provides integrated design guidance and recommendations for researchers and system developers. The book explores a recognized lack of integration between the HF and HCI research communities, which has led to inconsistencies between the research approaches adopted, and a lack of exploitation of research from one field by the other. The authors integrate theories and methodologies from these domains to provide design recommendations for human–machine developers. They then establish design guidance through the review of conceptual frameworks, analytical methodologies, and design processes for intelligent adaptive systems. The book draws on case studies from the military, medical, and distance learning domains to illustrate intelligent system design to examine lessons learned. Outlining an interaction-centered perspective for designing an IAS, the book details methodologies for understanding human work in complex environments and offers understanding about why and how optimizing human–machine interaction should be central to the design of IASs. The authors present an analytical and design methodology as well as an implementation strategy that helps you choose the proper design framework for your needs.