PARTE II: CONTRIBUCIÓN DE ESPAÑA A LAS DIRECTRICES ESTRATÉGICAS PARA UNA ACUICULTURA DE LA
O.1. Consensuar la planificación de la actividad y un marco normativo eficiente
The method of knowledge-based systems is perhaps the most notable AI paradigm to have found widespread application, particularly in process and manufacturing industries, and medical, business, education, and legal fields. This technique can represent a controller or a decision support system according to a number of architectures, and is able to handle heuristics and empirical knowledge. A basic definition of a knowledge-based system is a comprehensive computer program which solves problems within a limited and specific field, using data on the problem, knowledge related to the prob-lem, and “intelligent” decision-making capabilities. It typically emulates the problem-solving behavior of a human expert in the field; specifically, it tries to represent and execute the knowledge and reasoning strategy of one or more experts. In this context, it is known as an expert system. This is differ-ent from the convdiffer-entional algorithmic programming technique where the solution to a problem can be obtained by executing a prescribed set of steps according to a flowchart. Algorithmic programming offers fixed solutions to fixed (or “hard” or “crisp”) problems. On the other hand, a knowledge-based system is more suited to accomplish tasks where the nature of the problems and solutions is not well defined or not known beforehand. In industrial plants (say, in process control), for example, there are situations involving a significant number of variable factors such as changing plant characteristics, unexpected disturbances, system wear and tear, and different fault and alarm combinations, where the approach of knowledge-based systems has certain advantages and flexibility which can make the method particularly appropriate or attractive in such systems. For example, in the process control industry, systems with a large number of operating rules and constraints requiring complex logic are commonplace. As a result, know-ledge-based systems have great potential for successful application in the process control industry for handling a variety of applications requiring inference-making (or decision-making), including process monitoring, fault diagnosis, alarm management, and process scheduling and optimization. In some developments, knowledge-based systems have also been incorporated as part of the primary feedback loop, offering enhanced automatic and optimized control.
Figure 1.4 depicts the basic structure of a knowledge-based system (KBS).
There are four basic components: the knowledge base (or knowledge source), database, inference engine, and the system interface. A knowledge-based sys-tem is able to make perceptions (e.g., sensory perception) and new inferences or decisions using its reasoning mechanism (inference engine) by interpreting the meaning and implications of the new information within the capabilities of the existing knowledge base. These inferences may form the outputs of the knowledge-based systems. The associated decision-making task is an intelli-gent processing activity, which in turn may lead to enhancement, refinement, and updating of the knowledge base itself.
Central to a knowledge-based system is the so-called knowledge base.
The knowledge base contains knowledge and expertise in the specific
1.3 Knowledg e-ba sed sy s tems
13domain, particularly domain-specific facts and heuristics useful for solving problems in the domain. The knowledge source is implemented as an iden-tifiable and separate part of the program. This may be represented in various forms, and commonly as a set of if-then rules (called productions).
The second basic component is the database, which is primarily a short-term memory. The database contains the current status of the problem, inference states, and the history of solutions to date. New information that is generated or arrives from external sources such as sensors and human interfaces is stored in the database. This data represents the “context” of the decision-making process.
The inference engine is the “driver” program of a knowledge-based sys-tem. Depending on the data in the database, the inference engine applies and operates on the knowledge in the knowledge source to solve problems and arrive at conclusions. Specifically, it traverses the knowledge base, in response to observations and other inputs provided to it from the external world, and possibly previous inferences and results from the KBS itself, and will identify one or more possible outcomes or conclusions. This task of mak-ing inferences for arrivmak-ing at solutions will involve “processmak-ing” of know-ledge. It follows that representation and processing of knowledge are central to the functioning of a KBS. The inference engine will incorporate various inference mechanisms, for example forward chaining, backward chaining, and various search algorithms. The data structure selected for the specific form of knowledge representation determines the nature of the program created as an inference engine.
The fourth basic component of a KBS is the system interface (or user interface), which is provided for the external world (or user) to interact with the overall system, to browse through the knowledge source, to edit the knowledge (e.g., rules) and for many other interactive tasks. Keyboards, screen displays, sensors, transducers, and even output from other computer programs, including expert systems, usually provide the interface between a knowledge-based system and the external world.
Figure 1.4: The structure of a knowledge-based system
1 Introduction to intel lig ent sy s tems and sof t c omputing
14 Well-developed knowledge-based systems will also have additional cap-abilities such as a knowledge acquisition facility, and an explanation feature which can explain its reasoning and the decisions to the user. In the special case of production systems (i.e., rule based systems), the knowledge base consists of a set of rules written, for example, as an ASCII file. Therefore the knowledge acquisition facility that is required for these systems is often merely an editor. In systems based on other forms of representation, the knowledge acquisition facility will generally be an integral part of the KBS and can be used only with that system.
In the problem of knowledge-based decision-making, sensory informa-tion and any other available data on the process are evaluated against a knowledge base concerning the specific application, making use of an inference-making procedure. Typically, this procedure consists of some form of “matching” of the abstracted data with the knowledge base. In particular, for a knowledge base K and a set of data or information D on the particular process, the procedure of arriving at a decision or inference I may be expressed as
I= M[P(D), K] (1.1)
in which the “preprocessing operator” P(.) converts the context information on the process into a form that is compatible with K. Knowledge-base match-ing of the preprocessed information is performed usmatch-ing the matchmatch-ing opera-tion M[.]. Knowledge-based systems such as rule based systems and fuzzy decision-making systems in particular follow this model.
1.3.1 Architectures of knowledge-based systems
A knowledge-based system may be organized and developed according to one or a combination of several architectures. Three commonly used archi-tectures are:
(1) Production systems (2) Frame-based systems (3) Blackboard systems.
These three architectures have some commonalties and also some distin-guishing features. Let us outline some basic features of these architectures.
1.3.2 Production systems
Production systems are rule based systems, which are appropriate for the representation and processing of knowledge (i.e., knowledge-based decision-making) in problem solutions associated with artificial intelligence. An expert system, which will be discussed in detail in a subsequent section, is a good
1.3 Knowledg e-ba sed sy s tems
15 example of a production system. In a production system, knowledge isrepres-ented by a set of rules (or productions) stored in the knowledge base. The database contains the current data (or context) of the process. The inference engine is the reasoning mechanism, which controls rule matching, coordinates and organizes the sequence of steps used in solving a particular problem, and resolves any conflicts.
The operation (processing) of a typical rule based system proceeds as follows: new data are generated (say, from sensors or external commands) and stored in appropriate locations within the database of the system. This is the new context. The inference engine tries to match the new data with the condition part (i.e., the if part or the antecedent) of the rules in the knowledge base. This is called rule searching. If the condition part of a rule matches the data, that rule is “fired,” which generates an action dictated by the action part (i.e., the then part or the consequent) of the rule. In fact, firing of a rule amounts to the generation (inference) of new facts, and this in turn may form a context that will lead to the satisfaction (firing) of other rules.
Knowledge representation using a set of if-then rules is not an unfamiliar concept. For example, a maintenance or troubleshooting manual of a machine (e.g., automobile) contains such rules, perhaps in tabular form.
Also, a production system may be used as a simple model for human reason-ing: sensory data fire rules in the short-term memory, which will lead to the firing of more complex rules in the long-term memory.
Example 1.2
Consider a knowledge base for selecting a control technique, as given by the following set of rules:
If the plant is linear and uncoupled then use Control Category A.
If the plant is linear and coupled then use Control Category B.
If the plant is nonlinear use Control Category C.
If Category A and a plant model is known then use Subgroup 1.
If Category B and a plant model is known then use Subgroup 2.
If Subgroup 1 and high model uncertainty then use H-infinity control.
...
etc.
Now suppose that the database received the following context:
Linear Coupled Model Available Model Uncertainty High