An expert-system approach to the assembly planning of roller chains

In this paper, assembly planning of roller chains using an expert system is described. An Expert System Building Tool is used for the development of the expert system. Production rules are presented in OPS-5 language, in view of the information supporting the development of the expert system The frame-based representation of data, inference mechanism and conflict resolution strategy of the system are explained and discussed in detail.     

An integrated expert-system builder

The Expert System Builder, an AI tool kit implemented in Common Lisp on Symbolics workstations within ESPRIT Project 96 is described. The Expert System Builder system is intended to industrialize the process of building expert systems by providing developers with several powerful modeling and architectural facilities not found in most AI tool kits. To reach this goal, support for knowledge modularization and reusability were included in the system. The process of knowledge transfer is examined, as well as reasoning and control in the system. The Expert System Builder's concept system, model system, and building method are discussed. An example is given to illustrate its use     

An approach to capturing and reusing tacit design knowledge using relational learning for knowledge graphs

Tacit design knowledge plays an important role in the process of product design and is a valuable knowledge asset for enterprises. In terms of the characteristics of tacit rational design knowledge, this paper puts forward a scientific hypothesis and approach on capturing and reusing tacit rational design knowledge. The presented approach represents the observable design result facts of products using design knowledge graphs. A design issue-solving oriented knowledge graph model is presented, where directed relation edges represent design issues, and nodes stand for design solutions. When a new design solutions requirement needs to be searched, tacit design knowledge can be reused by relational learning for the constructed design knowledge graphs. In relational learning, the design knowledge graph is converted into a three-order tensor, where two modes are solution nodes, and the third mode holds the issue relations. Then, a tensor factorization approach is employed to calculate the latent features between design solutions for an issue relation. As a result, a score vector to represent the existence of issue-solution relations can be obtained. By sorting the scores in descending order, we may select the solution node with the highest score as the design solution to be searched. Finally, a stamping die design case study is provided. The case study shows that the proposed approach is feasible, and effective, and has better flexibility, scalability and efficiency than CBR methods.     

An approach to literate and structured formal developments

We present an approach to the literate and structured presentation of formal developments. We discuss the presentation of formal developments in a logical framework and distinguish three aspects: language-related aspects, structural aspects of proofs, and presentational aspects. We illustrate the approach by two examples: a simple mathematical proof of the Knaster-Tarski fixpoint theorem, and a formalization of the VDM development of a revision management system.     

An image annotation approach using location references to enhance geographic knowledge discovery

With the continuously increasing needs of location information for users around the world, applications of geospatial information have gained a lot of attention in both research and commercial organizations. Extraction of semantics from the image content for geospatial information seeking and knowledge discovery has been thus becoming a critical process. Unfortunately, the available geographic images may be blurred, either too light or too dark. It is therefore often hard to extract geographic features directly from images. In this paper, we describe our developed methods in applying local scale-invariant features and bag-of-keypoints techniques to annotating images, in order to carry out image categorization and geographic knowledge discovery tasks. First, local scale-invariant features are extracted from geographic images as representative geographic features. Subsequently, the bag-of-keypoints methods are used to construct a visual vocabulary and generate feature vectors to support image categorization and annotation. The annotated images are classified by using geographic nouns. The experimental results show that the proposed approach is sensible and can effectively enhance the tasks of geographic knowledge discovery.     

An MDA approach to knowledge engineering

This paper proposes an MDA approach to knowledge engineering, centered on the CommonKADS knowledge model. The latter corresponds to the CIM level of MDA whereas PRR, which represents production rules and rulesets, corresponds to the PIM level. The paper explores the mapping between CommonKADS knowledge models and production rules and rulesets based on PRR. Mapping CommonKADS knowledge models into PRR is very useful, due to the fact that the CIM level remains relatively unexplored, despite its key role in MDA. This motivates our choice to focus on the CIM and PIM levels. Furthermore, the mapping between PIM and PSM (i.e. the implementation of production rules in specific rule-based systems) constitutes less of an issue. To map CommonKADS knowledge models into PRR production rules and rulesets, we propose and illustrate a set of transformations. To ease these transformations, we start by grouping elements of the CommonKADS knowledge models into so-called “inference groups”. We propose and illustrate an algorithm that defines these inference groups automatically. The definition of transformations between models (CIM to PIM levels) requires a specific metamodel for CommonKADS as well as a dedicated metamodel for PRR. Unlike PRR, there is no published CommonKADS metamodel. This paper proposes a comprehensive CommonKADS knowledge metamodel. We describe and discuss an example, applying the whole approach.     

Algebraic approach to controller design using structured singular value

The aim of this paper is to show an algebraic approach to controller design using the structured singular value (denoted μ) as a robust stability and performance indicator. The algebraic μ-synthesis is applied to three different problems–time-delay systems, the HIMAT vehicle model and the two-tank system. A way of treating general delayed systems with uncertain time delays via the linear fractional transformation is shown. A simple controller is derived, which handles uncertain time delay in both the numerator and denominator of an anisochronic system. The overall performance is verified by simulations for all systems and compared with the D–K iteration.     

KASER: knowledge amplification by structured expert randomization

In this paper and attached video, we present a third-generation expert system named Knowledge Amplification by Structured Expert Randomization (KASER) for which a patent has been filed by the U.S. Navy's SPAWAR Systems Center, San Diego, CA (SSC SD). KASER is a creative expert system. It is capable of deductive, inductive, and mixed derivations. Its qualitative creativity is realized by using a tree-search mechanism. The system achieves creative reasoning by using a declarative representation of knowledge consisting of object trees and inheritance. KASER computes with words and phrases. It possesses a capability for metaphor-based explanations. This capability is useful in explaining its creative suggestions and serves to augment the capabilities provided by the explanation subsystems of conventional expert systems. KASER also exhibits an accelerated capability to learn. However, this capability depends on the particulars of the selected application domain. For example, application domains such as the game of chess exhibit a high degree of geometric symmetry. Conversely, application domains such as the game of craps played with two dice exhibit no predictable pattern, unless the dice are loaded. More generally, we say that domains whose informative content can be compressed to a significant degree without loss (or with relatively little loss) are symmetric. Incompressible domains are said to be asymmetric or random. The measure of symmetry plus the measure of randomness must always sum to unity.     

Temporal representation of clinical algorithms using expert-system and database tools

The HyperLipid Advisory System combines a rule-based implementation of a clinical algorithm (the NIH Cholesterol Education Program Expert Panel recommendations) with a temporal representation that facilitates reasoning over time while maintaining efficient storage in a standard database. The temporal representation consists of objects that model point events such as visits and interval events such as specific therapies. These objects are combined into abstractions called phases, which correspond to higher level clinical concepts such as a diet or drug treatment. The time-oriented data objects are referenced in the rules using a custom-tailored operator query language. Between user sessions relevant clinical data are stored in external files. When the advisory system is reconsulted, this information is retrieved and mapped back into an object-oriented format. Use of a commercially available expert-system shell for such tasks allows algorithm implementation in standard personal computing environments.     

An effective structured approach to finding optimal partitions of networks

The problem considered is to partition an edge-valued, node-weighted, finite, connected, simple graph (called a network) into disjoint subgraphs, each of which has a total node weight that does not exceed a weight constraint, and so that the total value of edges interconnecting the subgraphs is minimized. This paper presents a new approach which is effective in solving the problem, and fast enough to be practical in finding optimal partitions of large networks. It uses the concept of “divide and conquer” to partition the problem into several subproblems, which can be efficiently solved one after the other by using depth-first search and branch-and-bound principle. The operations required under the algorithms are additions, comparisons, and logical operations on binary vectors.     

MORSE: A general model to represent structured knowledge

In this paper, a model to represent knowledge (MORSE) that abstracts the structure of an automatic system is presented. This model is able to represent several ways of human reasoning, structured knowledge, execution strategies of an automatic system and many models presented in different works, such as hierarchical fuzzy controllers, cascade correlation neural networks architecture, decision trees, multilayer perceptrons, etc. Finally, thanks to the high level of abstraction of MORSE, the automatic systems specified by means of this model, have been classified in terms of their general features. This classification could allow a designer of systems to choose the best model of an automatic system to solve a problem. ©2000 John Wiley & Sons, Inc.     

An expert-system for the computer-assisted selection of bacterial strains for bioconversions

We present a rationale for building an expert system for the selection of bacterial strains for bioconversions; the main rule we use is the resemblance rule; this, in association with a data base including a thousand bioconversions, has been tested on steroids and has given good predictions in more than 70% of the cases we have tested.     

Heuristic knowledge representation of production scheduling: An integrated modeling approach

In spite of the efforts to automate production scheduling systems, most manufacturing plants rely on human schedulers for their practical production scheduling due mainly to the complexity and unpredictability of the manufacturing systems. One of the reasons for the difficulties in automating the production scheduling process is the lack of an explicit representation scheme of heuristic scheduling knowledge.

In this paper, an integrated modeling approach is presented to represent the complicated heuristic knowledge in production scheduling systems. The integrated modeling approach is based on object-oriented and event-driven modeling. It provides the explicit, declarative and executable representation of heuristic scheduling knowledge which aids the communication between human schedulers and system analysts. The declarative and executable representation of scheduling knowledge will enhance the flexibility of production scheduling systems and aid the development of production scheduling information systems.

A heuristic production scheduling model of a tire manufacturing system is used throughout the paper to illustrate the concepts and applicability of the approach.     

R-Calculus for ELP:An Operational Approach to Knowledge Base Maintenance

Sme concepts used in knowledge base maintenace,such as sequence,new law,user‘s rejection and reconstructions of a knowledge base,are first introduced,and then a framework for extended logic programming(ELP)is given,where an extended logic program is equivalent to a knowledge base.A transition system called R-calculus for ELP is provided.For a given knowledge base and a user‘s rejection,the R-calculus for ELP will deduce best revisions of the base.The soundness and the completeness of the R-calculus for ELP are proved,and the R-calculus for ELP is implemented in Prolog.In addition,the research is compared with other relevant work.     

An approach to learning mathematics through knowledge engineering

This study deals with an AI approach to learning mathematics, which is realised through the development of expert system knowledge bases by using programming in logic and PROLOG. The paper presents this approach, and examines its theoretical values in the light of recent findings in computer science, mathematics education, didactics, psychology and philosophy. The examined values show evidence for the relevance of the approach to the computer-assisted learning of mathematics.     

An intelligent approach of knowledge searching within Internet-based distributive knowledge integrated environment

Used to integrate all kinds of knowledge coming from various locations, domains and disciplines, a framework of the Internet-based distributive knowledge integrated system (DKIS) is proposed. Since knowledge repositories are the key components of the DKIS, the efficiency and reliability of the knowledge searching affects the running performance of the DKIS system. We stress methods of efficiently retrieving knowledge from DKIS repositories. Based on artificial neural networks, fuzzy logic and rule reasoning, a novel approach to knowledge searching over the Internet is presented. The issue of knowledge layering is also discussed. Using the rolling bearing as an example, a prototype of knowledge searching is realized. The main aim of this study is to identify an efficient and reliable method of knowledge retrieval for the DKIS within an Internet environment so as to support its perfect operation.     

An intrusion detection and alert correlation approach based on revising probabilistic classifiers using expert knowledge

Bayesian networks are important knowledge representation tools for handling uncertain pieces of information. The success of these models is strongly related to their capacity to represent and handle dependence relations. Some forms of Bayesian networks have been successfully applied in many classification tasks. In particular, naive Bayes classifiers have been used for intrusion detection and alerts correlation. This paper analyses the advantage of adding expert knowledge to probabilistic classifiers in the context of intrusion detection and alerts correlation. As examples of probabilistic classifiers, we will consider the well-known Naive Bayes, Tree Augmented Naïve Bayes (TAN), Hidden Naive Bayes (HNB) and decision tree classifiers. Our approach can be applied for any classifier where the outcome is a probability distribution over a set of classes (or decisions). In particular, we study how additional expert knowledge such as “it is expected that 80 % of traffic will be normal” can be integrated in classification tasks. Our aim is to revise probabilistic classifiers’ outputs in order to fit expert knowledge. Experimental results show that our approach improves existing results on different benchmarks from intrusion detection and alert correlation areas.     

SAFARI: a structured approach for automatic rule

This paper describes a new algorithm for obtaining rules automatically from training examples. The algorithm is applicable to examples involving both objects: with discrete and continuous-valued attributes. The paper explains a new quantization procedure fur continuous-valued attributes and shows how appropriate ranges of values of various attributes are obtained. The algorithm uses a decision-tree-based approach for obtaining rules, but unlike other tree-based algorithms such as ID3, it allows more than one attribute at a node which greatly improves its performance. The ability of the algorithm to obtain a measure of partial match further enhances its generalization characteristic. The algorithm produces the same rules irrespective of the order of presentation of training examples. The algorithm has been demonstrated on classification problems. The results have compared favorably with those obtained by existing inductive learning algorithms.