Problems of information overload on human operators of complex large-scale systems have been studied during the past few years mainly due to major accidents of aircrafts and in chemical, electrical, and industrial plants. Indeed, automatic controllers are not known to be subject to psychological stress phenomena. Yet, the same generic difficulty presents itself in automatic feedback control in a different form. The more input variables this optimization problem contains, the higher is the dimensionality of the search space, in which the optimal solution must be found. Human plant operators are kept aware of real-time constraints, reaching a decision always in time, yet possibly making a decision that may turn out to be inappropriate since the operator didn't have enough time to explore the entire search space, thereby overlooking both better solutions and undesired side effects of the one finally selected.
Automatic controllers are usually unaware of the passing of time.
Hence they can be made to always reach the optimal solution, however, the process may take too long for this solution to be of any practical use. On the other hand, if a controller is being equipped with a sense of time passing, it also must limit its search, and will then begin to make the same mistakes that human operators are bound to make. These intelligent systems are the topic of this dissertation. In particular, qualitative information has proven to be very useful in large-scale systems for discerning what is really going on, for deciding the state in which the system is at any point in time, and for assessing what would be the consequences of taking or not taking a proposed emergency action. Each section is composed of several chapters.