In May 1993, a task force was created at the invitation of the Technical Committee of Intelligent Control of the IEEE Control Systems Society to look into the area of Intelligent Control and define what is meant by the term. Its findings are aimed mainly towards serving the needs of the Control System Society; hence the task force has not attempted to address the issue of intelligence in its generality, but instead has concentrated on deriving working characterizations of Intelligent Control. Many of the findings however may apply to other disciplines as well.
The charge to the task force was to characterize intelligent control systems, to be able to recognize them and distinguish them from conventional control systems; to clarify the role of control in intelligent systems and to help identify problems where intelligent control methods appear to be the only viable avenues.
In accomplishing these goals, the emphasis was on working definitions and useful characterizations rather aphorism. It was accepted early on that more than one definition of intelligent system may be necessary, depending on the view taken and the problems addressed.
In the following a brief description of the findings is given. The relation to autonomy is that intelligent control methods are used to achieve high levels of autonomy in systems.
A.1 Intelligence and Intelligent Control
It is appropriate to briefly comment on the meaning of the word intelligent in “intelligent control.” Note that the precise definition of “intelligence” has been eluding mankind for thousands of years. More recently, this issue has been addressed by disciplines such as psychology, philosophy, biology and of course by Artificial Intelligence (AI); note that AI is defined to be the study of mental faculties through the use of computational models. No consensus has emerged as yet of what constitutes intelligence. The controversy surrounding the widely used IQ tests, also points to the fact that we are well away from having understood these issues. In this Appendix, we discuss several characterizations of intelligent systems that appear to be useful when attempting to address complex control problems. Intelligent systems can be seen as machines which emulate human mental faculties such as adaptation and learning, planning under large uncertainty, coping with large amounts of data etc. in order to effectively control complex processes; and this is the justification for the use of the term intelligent in intelligent control, since these mental faculties are considered to be important attributes of human intelligence. An alternative term, that was discussed in this article, is “autonomous (intelligent) control;” it emphasizes the fact that an intelligent controller typically aims to attain higher degrees of autonomy in accomplishing and even setting control goals, rather than stressing the (intelligent) methodology that achieves those goals. We should keep in mind that “intelligent control” is only a name that appears to be useful today (this was the comment made over 20 years ago and has proven to be correct). In the same way, the “modern control” of the 60’s has now become “conventional (or traditional) control,” as it has become part of the mainstream, what is called intelligent control today may be called just “control” in the not so distant future (which is exactly what has happened). What is more important than the terminology used are the concepts and the methodology, and whether or not the control area and intelligent control will be able to meet the ever-increasing control needs of our technological society.
A.2 Defining Intelligent Control Systems
Intelligent systems can be characterized in a number of ways and along a number of dimensions. There are certain attributes of intelligent systems, which are of particular interest in the control of systems [2, 3]. We begin with a general characterization of intelligent systems: An intelligent system has the ability to act appropriately in an uncertain environment, where an appropriate action is that which increases the probability of success, and success is the achievement of behavioral sub-goals that support the system’s ultimate goal. In order for a man-made intelligent system to act appropriately, it may emulate functions of living creatures and ultimately human mental faculties. An intelligent system can be characterized along a number of dimensions. There are degrees or levels of intelligence that can be measured along the various dimensions of intelligence. At a minimum, intelligence requires the ability to sense the environment, to make decisions and to control action. Higher levels of intelligence may include the ability to recognize objects and events, to represent knowledge in a world model, and to reason about and plan for the future. In advanced forms, intelligence provides the capacity to perceive and understand, to choose wisely, and to act successfully under a large variety of circumstances so as to survive and prosper in a complex and often hostile environment. Intelligence can be observed to grow and evolve, both through growth in computational power and through accumulation of knowledge of how to sense, decide and act in a complex and changing world. The above characterization of an intelligent system is rather general. According to this, a great number of systems can be considered intelligent. In fact, according to this definition even a thermostat may be considered to be an intelligent system, although of low level of intelligence. It is common however to call a system intelligent when in fact it has a rather high level of intelligence. There exist a number of alternative but related definitions of intelligent systems, which emphasize systems with high degrees of intelligence. For example, the following definition emphasizes the fact that the system in question processes information, and it focuses on man-made systems and intelligent machines: Machine intelligence is the process of analyzing, organizing and converting data into knowledge; where (machine) knowledge is defined to be the structured information acquired and applied to remove ignorance or uncertainty about a specific task pertaining to the intelligent machine. This definition relates to the principle of increasing precision with decreasing intelligence of Saridis. Next, an intelligent system can be characterized by its ability to dynamically assign sub-goals and control actions in an internal or autonomous fashion: Many adaptive or learning control systems can be thought of as designing a control law to meet well-defined control objectives. This activity represents the system’s attempt to organize or order its “knowledge” of its own dynamical behavior, so to meet a control objective. The organization of knowledge can be seen as one important attribute of intelligence. If this organization is done autonomously by the system, then intelligence becomes a property of the system, rather than of the system’s designer. This implies that systems which autonomously (self)-organize controllers with respect to an internally realized organizational principle are intelligent control systems. A procedural characterization of intelligent systems is given next: Intelligence is a property of the system which emerges when the procedures of focusing attention, combinatorial search, and generalization are applied to the input information in order to produce the output. One can easily deduce that once a string of the above procedures is defined, the other levels of resolution of the structure of intelligence are growing as a result of the recursion. Having only one level structure leads to a rudimentary intelligence that is implicit in the thermostat, or to a variable-structure sliding mode controller.
A.3 Control and Intelligent Systems
The concepts of intelligence and control are closely related and the term “Intelligent Control” has a unique and distinguishable meaning. An intelligent system must define and use goals. Control is then required to move the system to these goals and to define such goals. Consequently, any intelligent system will be a control system. Conversely, intelligence is necessary to provide desirable functioning of systems under changing conditions, and it is necessary to achieve a high degree of autonomous behavior in a control system. Since control is an essential part of any intelligent system, the term “intelligent control systems” is sometimes used in engineering literature instead of “intelligent systems” or “intelligent machines.” The term “intelligent control system” simply stresses the control aspect of the intelligent system. Below, one more alternative characterization of intelligent (control) systems is included. According to this view, a control system consists of data structures or objects (the plant models and the control goals) and processing units or methods (the control laws): An intelligent control system is designed so that it can autonomously achieve a high-level goal, while its components, control goals, plant models and control laws are not completely defined, either because they were not known at the design time or because they changed unexpectedly.
A.4 Characteristics or Dimensions of Intelligent Systems
There are several essential properties present in different degrees in intelligent systems. One can perceive them as intelligent system characteristics or dimensions along which different degrees or levels of intelligence can be measured. Below we discuss three such characteristics that appear to be rather fundamental in intelligent control systems.
Adaptation and Learning
The ability to adapt to changing conditions is necessary in an intelligent system. Although adaptation does not necessarily require the ability to learn, for systems to be able to adapt to a wide variety of unexpected changes learning is essential. So, the ability to learn is an important characteristic of (highly) intelligent systems.
Autonomy and Intelligence
Autonomy in setting and achieving goals is an important characteristic of intelligent control systems. When a system has the ability to act appropriately in an uncertain environment for extended periods of time without external intervention it is considered to be highly autonomous. There are degrees of autonomy; an adaptive control system can be considered as a system of higher autonomy than a control system with fixed controllers, as it can cope with greater uncertainty than a fixed feedback controller. Although for low autonomy no intelligence (or “low” intelligence) is necessary, for high degrees of autonomy, intelligence in the system (or “high” degrees of intelligence) is essential.
Structures and Hierarchies
In order to cope with complexity, an intelligent system must have an appropriate functional architecture or structure for efficient analysis and evaluation of control strategies. This structure should provide a mechanism to build levels of abstraction (resolution, granularity) or at least some form of partial ordering so to reduce complexity. An approach to study intelligent machines involving entropy (of Saridis) emphasizes such efficient computational structures. Hierarchies (that may be approximate, localized or combined in heterarchies) that are able to adapt, may serve as primary vehicles for such structures to cope with complexity. The term “hierarchies” refers to functional hierarchies, or hierarchies of range and resolution along spatial or temporal dimensions, and it does not necessarily imply hierarchical hardware. Some of these structures may be hardwired in part. To cope with changing circumstances the ability to learn is essential so these structures can adapt to significant, unanticipated changes.
In view of the above
a working characterization of intelligent systems (or of (highly) intelligent (control) systems or machines) that captures the essential characteristics present in any such system is: An intelligent system must be highly adaptable to significant unanticipated changes, and so learning is essential. It must exhibit high degree of autonomy in dealing with changes. It must be able to deal with significant complexity, and this leads to certain types of functional architectures such as hierarchies.