Knowledge based systems

The EROS (Elaboration of Reactions for Organic Synthesis) system [26] is a knowledge-based system which was created for the simulation of organic reactions. Given a certain set of starting materials, EROS investigates the potential reaction pathways. It produces sequences of simultaneous and consecutive reactions and attempts to predict the products that will be obtained in those reactions. 

Correlations between structure and mass spectra were established on the basis of multivariate analysis of the spectra, database searching, or the development of knowledge-based systems, some including explicit management of chemical reactions. 

Ia the 1990s robotics guided by artificial iateUigeace are expected to play a role comparable to that of electronics in the 1940s and 1950s (6). Expert systems, which are knowledge-based systems that can effectively represent and apply factual knowledge in specific areas of human expertise, seem ideaUy suited to robot supervision. 

Computer-aided process synthesis systems do not mean completely automated design systems (57). Process synthesis should be carried out by interactive systems, in which the engineer s role is to carry out synthesis and the machine s role is to analy2e the performance of synthesized systems. Computet apphcations in the future will probably deal with the knowledge-based system in appHed artificial intelligence. Consequendy, research on computer-aided process synthesis should be directed toward the realization of such systems with the collaboration of experienced process engineers. 

Definition 2 Knowledge-based systems are computer programs that encode symboHc knowledge about domains and tasks, and solve problems by manipulating this knowledge using quaHtative techniques. 

Definition 2 is phrased in terms of knowledge-based systems rather than expert systems. No reference is made to expert human problem solvers. Definition 2 captures the sense that the representation and manipulation of knowledge is the source of such a system s power, whether or not that knowledge is dkecdy eHcited from a human expert. 

These two examples hint at a few of the reasons for the importance of knowledge-based systems. A medical faciHty may handle hundreds of infectious disease cases a year. Speedy, accurate diagnosis of these cases, aided by a system such as Mycin, may help the medical faciHty handle more patients, more effectively. Likewise, configuring large computer systems composed of many components can be a time-consuming and error-prone task. 

Erom a technology perspective, knowledge-based systems (KBS) represent a new software methodology for solving certain types of problems effectively. It is important to understand what is encoded in knowledge-based systems, and how KBS technology differs from conventional numeric computational techniques. 

Deduction. If a knowledge-based system has a set of facts, and new facts are provided to it, then rules of inference can be appHed to the set of facts to derive conclusions. For example, from the facts that (/) hydrogen and oxygen can react explosively at high temperatures, (2) air contains oxygen,... 

J) the atmosphere inside a particular reactor contains hydrogen, and (4) the reactor is at a high temperature, the additional fact that air has leaked into the feed to the reactor leads to the conclusion that an explosion can take place. The conclusion is based on applying deductive logic to the known facts. This is representative of the logic-based approach to knowledge-based systems. 

Knowledge-based systems typically use quaHtative methods rather than quantitative ones. For example, consider a simple tank system. The equation describing the flow rate of Hquid out of the tank is given below, where C is the orifice coefficient, d is the diameter of the orifice, and h is the height of Hquid in the tank. Based solely on the form of the equation, a human reasoner can infer that the flow rate F increases monotonically with the height b of Hquid in the tank. 

Rules. Rules, first pioneered by early appHcations such as Mycin and Rl, are probably the most common form of representation used in knowledge-based systems. The basic idea of rule-based representation is simple. Pieces of knowledge are represented as IE—THEN rules. IE—THEN rules are essentially association pairs, specifying that IE certain preconditions are met, THEN certain fact(s) can be concluded. The preconditions are referred to as the left-hand side (LHS) of the rule, while the conclusions are referred to as the right-hand side (RHS). In simple rule-based systems, both the... 

Of these, the first two place demands on the processing speed of the knowledge-based system and its integration with the environment, eg, data acquisition interfaces. The third impacts on the nature of the computational cycle and the system s abiHty to reschedule its own computations. The latter two place demands on the representation and reasoning capabiHties of the system, and are the most interesting from a knowledge-based system viewpoint. 

Technical and Business Issues. In general, the successflil appHcation of any technology is dependent on both technical and business issues. For knowledge-based systems, six critical issues are problem identification, user acceptance, measurement of impact, appropriateness, feasibHity, and cost. 

Alternative technologies should also be considered, and the reasons for not using them should be justifiable. For example, database technology is not the right choice if a task requires reasoning that goes beyond retrieval of stored data based on well-defined criteria. At the same time, many problems that are stated in a symboHc way can be formulated mathematically, and in fact can be better solved numerically. For such problems, knowledge-based systems are not the appropriate answer. 

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