Knowledge machine

Knowledge machine (KM) is a frame-based knowledge representation langnage similar to KRL and other KL-ONE representation languages such as Loom and CLASSIC [13-15], In KM, a frame denotes either a class (i.e., type) or an instance (i.e., individual). Frames have slots, or binary predicates, in which the fillers are axioms about the slot s value. These axioms have both declarative and procedural semantics, allowing for procedural inference. 

For example, the class Steroid could look as follows  

We can then generate a steroid instance and query the slots by using the following syntax  

The first expression creates an anonymous instance, or Skolem constant, from the class Steroid at run time, denoted by the underscore and an instance number. These anonymous instances are also created for the Spectrum and the Descriptor. A named instance can be created by 

This type of query has a single access path, since it derives its answer with a single step. A query with an access path of 2 would look as follows  

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Clark, P. and Porter, B., KM — The Knowledge Machine Users Manual, http //www. cs.utexas.edu/users/mfkb/km/userman.pdf. 

One of the first attempts to build a knowledge base for synthetic organic reactions was made by Gelernter s group, through inductive and deductive machine learning [1]. Important work on this topic was also performed by Funatsu and his group [2]. 

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. 

Thermocompression Evaporators Thermocompression-evap-orator calculations [Pridgeon, Chem. Metall. Eng., 28, 1109 (1923) Peter, Chimin Switzerland), 3, II4 (1949) Petzold, Chem. Ing. Tech., 22, 147 (1950) and Weimer, Dolf, and Austin, Chem. Eng. Prog., 76(11), 78 (1980)] are much the same as single-effect calculations with the added comphcation that the heat suppied to the evaporator from compressed vapor and other sources must exactly balance the heat requirements. Some knowledge of compressor efficiency is also required. Large axial-flow machines on the order of 236-mVs (500,000-ftVmin) capacity may have efficiencies of 80 to 85 percent. Efficiency drops to about 75 percent for a I4-mVs (30,000-ftVmin) centrifugal compressor. Steam-jet compressors have thermodynamic efficiencies on the order of only 25 to 30 percent. 

This field is very large and a detailed study of the subject is beyond the scope of this handbook. We will limit our discussions to the area of this subject that relates to the control of a.c. motors and attempt to identify the different solid-state devices that have been developed and their application in the control of a.c. motors. ()nly the more common circuits and configurations are discussed. The brief discussion of the subject provided here, however, should help the reader to understand this subject in general terms and to use this knowledge in the field of a.c. motor controls to achieve from a soft start to a very precise speed control and, more importantly, to conserve the energy of the machine which would be wasted otherwise. For more details of. statie controllers see the Further reading (Sr. nos. 2, 4., 5, 8 and 12) at the end of the chapter. To... 

Rasmussen, J, 1979, On the Structure of Knowledge A Morphology of Mental Models in a Man-Machine Context, Riso-M-2192, Riso Nat. Lab., Denmark. 

Machine learning can analyze a large dataset and determine what information is most pertinent. Such generalized information can then be converted into knowledge through the generation of rule sets that will enable faster and more relevant decisions. 

Archimedes mechanical skill, together with his theoretical knowledge, enabled him to construct many ingenious machines. During his time in Egypt, he invented a hand-cranked manual pump, known as Archimedes screw, that is still used in many parts of the world. Its open structure is capable of lifting fluids even if they contain large amounts of debris. 

This chapter provides the basic knowledge and skills required to implement a computer-based vibrationmonitoring program. It discusses the following topics (1) typical machine-train monitoring parameters, (2) database development, (3) data-acquisition equipment and methods, and (4) data analysis. 

Similar to reliability work, bad actors are also identified through knowledge of the machinery MTBF by type, unit and service. Resources are likely to be limited, which means we will not be able to investigate all resources that deviate from an acceptable or expected time to failure (TTF). Therefore, the next step is to carefully choose an acceptable TTF for machine breakdown, keeping these limitations in mind. We don t want to start at the top, but it is imperative that we remember to use this process to cover all failures, even if we feel we know all of the bad actors. Later, the importance of this step will become obvious because it will not be as easy for us to tell the bad actors from acceptable actors. Figure 62.3 shows the methodology of this process. 

The term process control is often used when machine control is actually performed. As the knowledge base of the fundamentals of the molding process continues to grow,... 

The predictions of the Third Law have been verified in a sufficiently large number of cases that experimental attempts to reach absolute zero are now placed in the same class as attempts to devise perpetual motion machines — which is to say there are much more productive ways to spend one s time. Much experimental work is carried out. however, at very low temperatures, because the behavior of matter under these conditions has produced many surprises and led to the uncovering of a great deal of new knowledge and the development of useful new devices, such as superconducting magnets.cc... 

Rather than trying to replace any of the above traditional techniques, this chapter presents the development of complementary frameworks and methodologies, supported by symbolic empirical machine learning algorithms (Kodratoff and Michalski, 1990 Shavlik and Dietterich, 1990 Shapiro and Frawley, 1991). These ideas from machine learning try to overcome some of the weaknesses of the traditional techniques in terms of both (1) the number and type of a priori decisions and assumptions that they require and (2) the knowledge representation formats they choose to express final solutions. 

The last step in the preceding argument, the use of our knowledge about flowshop scheduling, turns what had been a mainly syntactic criterion over the tree structure of the example, into a criterion based on state variables of (jc, y). The state variable values, the completion times of the various flowshop machines, are accessible before the subtrees beneath jc and y have been generated. Indeed, they determine the relationships between the respective elements of the subtrees (jcm, yu). If we can formalize the process of showing that the pair (jc, y) identified with our syntactic criterion, satisfies the eonditions for equivalence or dominance, wc will in the process have generated a new equivalence rule. 

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