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The increasing complexity of parallel computing systems has brought about a crisis in parallel performance evaluation and tuning. Tools for performance measurement and visualization become necessary parts of programming environments for parallel computers. In this paper we describe a tool — which we call the Performance Adviser — that offers two different levels of performance information (high level and low level), guides the users to specific problem areas in the source code, and suggests actions to improve the performance of their parallel programs. Working behind the Performance Adviser is an expert system which derives high level concepts from the source code and a critical path analysis metric which derives low level performance information from the performance data collected in the execution of the program.

In this paper, an expert system is presented that was developed with the aim to analyze threshold spectra experimentally obtained using backward chaining method of inference. With this way the process of spectra analysis was speeded up and automated. The spectrum usefulness, energy scale calibration, identification of peaks in spectrum, as well as identification of peaks by resonant influence was made possible with this expert system.

The electron method spectroscopy consists in the detection of very low energy electrons formed in electron–molecule collisions. The advantage of this method over the method of optical spectroscopy lies in the possibility to study optically forbidden states, so the complexity of spectrum rises steeply, which makes its analysis more difficult. Besides electron states, there are also a vibrational states in threshold spectra analysis, and those had to be identified and classified. This paper presents an expert system developed to automate, speed up and improve the N₂ threshold spectra analysis.

In this paper an expert system is presented that was developed with the aim to analyze electron spectra on the threshold energies in collision with triatomic and polyatomic molecules. This expert system was made by updating the expert system for the analysis of the threshold spectra of two-atomic molecules. In contrast to the threshold spectra of two-atomic molecules, where were exist more or less defined structures of excited states, as well as vibrational levels, in the threshold spectra of triatomic molecules clearly defined structures almost do not exist. The application of the expert system in the process of triatomic and polyatomic molecules spectra analysis has an important role because it enables analysis of larger number of excited states, vibrational levels, and resonances, which can be missed in the analysis by classical methods, either because of low intensity or because of insufficiently defined separation of peaks in spectrum, which arise from the overlapping of a large number of peaks.

In this paper is described an expert system (ES) developed in order to enable the analysis of emission spectra, which are obtained by measurements of activities of radioactive elements, i.e., isotopes, actually cesium. In the structure of those spectra exists two parts: first on lower energies, which originates from the Compton effect, and second on higher energies, which contains the photopeak. The aforementioned ES is made to perform analysis of spectra in whole range of energies. Analysis of those spectra is very interesting because of the problem of environmental contamination by radio nuclides.

A knowledge model for knowledge base system in the Web environment is proposed, which includes problem description layer and knowledge layer. A knowledge base is made up of a set of knowledge models. Knowledge models are used to support knowledge representation and reasoning. Visual knowledge modeling tool and visual knowledge service tool based on Web environment realize the construction of knowledge base system in Web environment. The knowledge base system supports the construction of agricultural expert system. By comparison with CommonKADS, it is proved that this method can improve the efficiency of knowledge acquisition, management and maintenance in Web environment. This method also has the reference significance to the construction of the knowledge base system in the Web environment in other domains.

This paper describes EJAUNDICE, which is designed to be a general-purpose expert system building tool. Considerations behind a number of design decisions for purposes of generality are examined. EJAUNDICE provides several control schemes, including biphasical control with goal-directed reasoning, data-driven processing, and control blocks, and integrates rule-based, frame-based, and logic-based reasoning paradigms in its framework.

The issues of knowledge representation and input/output in developing a Chinese expert system are also investigated.

In the case of expert systems, when premises from two different rules argue for the same conclusion, there exist a number of methods for combining the certainties in the conclusion, obtained from each premise individually, to yield a total certainty in the conclusion. When these certainties are interpreted as probability values, it is necessary in all these methods to make assumptions of independence concerning the premises and conclusion. It is proven here that there does not exist a procedure which yields, for all probability functions, meaningful bounds for the probability of the conclusion based on two premises in terms of the probabilities of the conclusion based on each premise individually. Additionally, it is proven that there does not even exist a procedure which yields, for all probability functions, meaningful bounds for the probability of the conclusion based on two premises in terms of the probabilities of all one- and two-member combinations of the premises and the conclusion.

Finally, suggestions are made as to when to make assumptions of independence and when to take other alternatives.

This paper presents the design and implementation of a Chinese Expert System Shell which is based on a Chinese Prolog interpreter.

The system is divided into three parts: the knowledge acquisition module, the knowledge application module and the inference engine.

The knowledge engineer defines the syntax of the language to be used by himself and by the users when they interact with the system. The natural language interface is table driven and can be modified easily.

The system also caters for the case when the domain expert finds it difficult to articulate the rules, but is able to give examples. An inductive engine is included to extract rules from examples.

We are interested in the semantic interpretation of 3-D data obtained by a stereovision algorithm, in the context of indoor scenes. This paper presents a high level process built to find the main plane structures of the scene and to label them with a semantic description. This process is made up of three stages: first, the sorting and enhancement of the input data (3-D segments), second, the generation of affine planes from the sorted 3-D segments and finally, the semantic description of these planes by a classification expert system using the interpreter CLASSIC. This process has been tested on various stereo images and the quality of the results, in robustness and accuracy, are quite good. The advantages of using a knowledge based system are discussed.

A definition of expert systems is given, its pragmatic demands are cited and its structure is described. The methods and ways of pattern recognition are used in the subsystems DIALOGUE, ANALYTIC and HOMEOSTAT. The recognition algorithms which work on the information to be retained in the data base and knowledge base are described. The problems of recognition appearing under the construction of expert systems are noted.

We consider the class of logical decision rules and its applications for the solution of various problems of multivariate statistical analysis: discriminant and regression analysis, and cluster analysis. Some useful properties of the statistical analysis methods using the class under consideration are shown. Particular attention is paid to the possibility of presenting statistical results (unlike all other methods) in a language close to a natural language of logical statements.

Thermal infrared images of the ocean obtained from satellite sensors are widely used for the study of ocean dynamics. The derivation of mesoscale ocean information from satellite data depends to a large extent on the correct interpretation of infrared oceanographic images. The difficulty of the image analysis and understanding problem for oceanographic images is due in large part to the lack of precise mathematical descriptions of the ocean features, coupled with the time varying nature of these features and the complication that the view of the ocean surface is typically obscured by clouds, sometimes almost completely. Towards this objective, the present paper describes a hybrid technique that utilizes a nonlinear probabilistic relaxation method and an expert system for the oceanographic image interpretation problem. This paper highlights the advantages of using the contextual information in the feature labeling algorithm. The need for an expert system and its feedback in automatic interpretation of oceanic features is discussed. The paper presents some important results of the series of experiments conducted at the Remote Sensing Branch, of the Naval Oceanographic and Atmospheric Research Laboratory, on the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (AVHRR) imagery data. The results clearly indicate the drastic improvement in labeling due to the oceanographic expert system.

Even though AI technology is a relatively new discipline, many of its concepts have already found practical applications. Expert systems, in particular, have made significant contributions to technologies in such fields as business, medicine, engineering design, chemistry, and particle physics.

This paper describes an expert system developed to aid mechanical engineering designers in the preliminary design of variable-stroke internal-combustion engines. Variable-stroke engines are more economical in fuel consumption but their design is particularly difficult to accomplish. With the traditional design approach, synthesizing the mechanisms for the design is rather difficult and evaluating the mechanisms is an even more cumbersome and time-consuming effort. Our expert system assists the designer by generating and evaluating a large number of design alternatives represented in the form of graphs. Through the application of structural and design rules obtained from design experts to the graphs, good quality preliminary design configurations of the engines are promptly deduced. This approach can also be used in designing other types of mechanisms.

A connectionist expert system model is proposed in this paper. The system combines the capability of production systems and associative memory. Rules are explicitly represented within a network structure. The Perceptron algorithm is generalized to include samples with uncertain components. A gradually-augmented-node learning algorithm is used to guarantee fast memorization of all rules. The convergence of the learning algorithms is analyzed. The system has a dynamic knowledge organization and adapts in real time to acquire new knowledge, or to relearn existing knowledge, through interaction with the user. This allows the system to be built incrementally. An example system is presented.

A major step in the process of speech understanding is the acoustic-phonetic decoding which can be defined as the automatic mapping of the continuous speech wave into a set of predetermined linguistic units such as phones, diphones, syllables, etc. This paper relates to the approach of this problem which consists in exploiting an explicit description of all kinds of available knowledge about the speech communication phenomena, in the general framework of an artificial intelligence knowledge-based system.

We will first recall the main difficulties of acoustic-phonetic decoding with a practical example. We will then present the APHODEX system that we have been designing for the past eight years, in terms of software architecture and of knowledge representation and reasoning. The practical evaluation of this system will then be carried out at the different levels of feature extraction, segmentation and labelling. Finally, we will discuss the limitations of our approach and present the ongoing effort to overcome these limitations, especially through the use of abductive reasoning.

In this paper we present a VLSI fuzzy processor whose main features are a scalable parallel architecture, and the computation of fuzzy inferences based on the α-level set theory, both of which are important in the field of intensive fuzzy computing, as in fuzzy expert systems. A specific analysis is made in the paper, of techniques for the representation of fuzzy sets, in relation to the amount of area occupied and the forms they can assume. From this analysis a solution is extracted and then used for the processor presented in the paper.

The architecture of the processor is chosen after the assessment of possible alternatives by analyzing an appropriate probabilistic model. The processor comprises a set of units which work parallelly and asynschronously to process the various rules. The structure is easy to scale up, as an increase in the number of processing units does not produce bottlenecks in performance. The performance obtainable is about 310 KFLIPS, with a clock frequency of 60 Mhz, 8 input variables, either crisp or fuzzy, and an 8-bit resolution.

Knowledge about relations plays a crucial role in human’s knowledge. Different methods for representing this type of knowledge have been proposed. However, due to the lack of theoretical foundations, these methods cannot guarantee criteria in knowledge representation such as formality, universality, usability and practicality. They are not adequate to represent the knowledge domains in practice which have many components. Based on formal ontology approach, a knowledge model about relations, called Rela-model, is presented in this paper. It has the components such as concepts, relations between concepts, and rules. The concepts in this model consist of attributes, facts and rules of itself. Each object in a concept has also equipped its behavior to solve problems on it. The methods for solving problems based on Rela-model are also studied. The general problems on this model are the following: Given some objects and facts on them, determine the closure of set of attributes and facts on the objects or determine an object or consider a relation between the objects. The algorithms to solve problems are designed and their properties, such as finiteness, effectiveness, have also been proved. Besides the solid mathematical foundation, Rela-model also has a simple specification language which can effectively represent the knowledge, thus it can be used in many real situations. Our approach is also applied to build two systems: the intelligent problem solver about solid geometry in high school mathematics, and the expert system to diagnose diseases in diabetic microvascular complication.

The neural network discussed in this paper is a self trained network for LArge Memory STorage And Retrieval (LAMSTAR) of information. It employs features such as forgetting, interpolation, extrapolation and filtering, to enhance processing and memory efficiency and to allow zooming in and out of memories. The network is based on modified SOM (Self-Organizing-Map) modules and on arrays of link-weight vectors to channel information vertically and horizontally throughout the network. Direct feedback and up/down counting serve to set these link weights as a higher-hierarchy performance evaluator element which also provides high level interrupts. Pseudo random modulation of the link weights prevents dogmatic network behavior. The input word is a coded vector of several sub-words (sub-vectors). These features facilitate very rapid intelligent retrieval and diagnosis of very large memories, that have properties of a self-adaptive expert system with continuously adjustable weights. The authors have applied the network to a simple medical diagnosis and fault detection problems.