ABSTRACT
All science is about dynamics, i.e., how everything changes. What it takes to understand and adapt to change is best embodied in the functioning of the living. Therefore, it is not surprising that knowledge of life processes guide the effort to provide nature-like means and methods for dealing with change, moreover for predicting it. Science expressed computationally integrates life-inspired knowledge, as well as faster and more diverse processing of data pertinent to change. Integration of life science and technological performance is a prerequisite for both predictive and anticipatory computing. Thus the goals pursued herein are
1. To present efficient processing models that describe the various levels at which the future state of a system can be effectively represented;
2. To address specific forms through which predictive computation is performed; and
3. To define progress toward anticipatory computation.
