Visual ObservationforHybrid Intelligent
Previous: Error States and
The underlying mathematical representations of complex computer-controlled systems is still insufficient to create a set of models which accurately captures the dynamics of the system over the entire range of system operation. We remain in a situation where we must tradeoff the accuracy of our models with the manageability of the models. Closed-form solutions of mathematical models are almost exclusively limited to linear system models. Computer simulations of nonlinear and discrete-event models provide a means for off-line design of control systems through iterative search but such simulations cannot perform exhaustive search due to the complexity of the problem. Guarantees of system performance are limited to those regions where the robustness conditions apply. These conditions may not apply during startup and shutdown or during periods of anomalous operation. Excellent results are available for cases where adequate mathematical models are known and the system is operating ``close enough'' to a linear region. Also, effective tools are available to model high-level system changes as a finite state machine. Several attempts to improve our modeling capabilities are focused on mapping the continuous world into a discrete one However, repeated results are available which indicate that large interactive systems evolve into states where minor events can lead to a catastrophe. We are left with the result that there is a pressing need for a more adequate theory and mathematical basis for representing and predicting the performance of hybrid dynamical systems. Some current work has focused on providing a mathematical basis for the coupling of numerical and symbolic computing [3,5]. In the near term we will probably be able to mathematically prove (automatically verify) that the implementation of a subset of software for computer-controlled systems performs to specifications but will have to use conventional metrics for verification of the majority of the software being used. In this paper we have summarized a new framework and representation for the general problem of observation, emphasizing its' application to determining error states and sequences for the highest levels of computer-controlled systems. We assert that the framework provides a means for the explicit realization of transitions from low-level to high-level, goal-oriented knowledge in computer-controlled systems.
Visual ObservationforHybrid Intelligent
Previous: Error States and