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Issue 2 • Date April 1993

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Displaying Results 1 - 9 of 9
  • Knowledge-based and statistical approaches to text retrieval

    Page(s): 8 - 12
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    Major research issues in information retrieval are reviewed, and developments in knowledge-based approaches are described. It is argued that although a fair amount of work has been done, the effectiveness of this approach has yet to be demonstrated. It is suggested that statistical techniques and knowledge-based approaches should be viewed as complementary, rather than competitive.<> View full abstract»

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  • Using statistical methods to improve knowledge-based news categorization

    Page(s): 13 - 23
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    NLDB, a knowledge-based system that automatically categorizes news stories for dissemination, retrieval, and browsing, is discussed. The major knowledge-based component of NLDB is a lexicosemantic pattern matcher that identifies combinations of words and phrases, as well as more complex patterns. These include word roots, grammatical categories, and semantic structures, such as verbs describing classes of events. It is shown that this linguistic analysis outperforms statistical methods. Because building lexicosemantic patterns can be a laborious process, a set of statistical methods that automate pattern acquisition while preserving the benefits of a knowledge-based approach are developed.<> View full abstract»

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  • Generating, integrating, and activating thesauri for concept-based document retrieval

    Page(s): 25 - 34
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    Freely Available from IEEE
  • Self-modeling databases

    Page(s): 35 - 43
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    The Carper system, which uses inductive learning to check database consistency, even in poorly understood domains, is described. The application of Carper to the Xcon expert system database is discussed. It is shown that Carper can detect five general error types in Xcon: using value naming conventions inconsistently, assigning legal but incorrect values to attributes, omitting obscure but necessary attribute values, assigning values to attributes that should be left undefined, and failing to update attribute values when dependent attribute values change.<> View full abstract»

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  • Diagnosing dynamic faults using modular neural nets

    Page(s): 44 - 53
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    The use of radial basis function networks (RBFNs) for diagnosis and classification is discussed. Even though RBFNs can be trained quickly compared to backpropagation networks, the training effort is still significant for large-scale diagnosis problems. Rho-Net, an architecture that decomposes the dynamic classification problem in two ways, making such training tractable, is presented. The first decomposition reduces the amount of training data needed for any stage of the training process by constructing separate networks for each fault class. The second decomposition reduces the dimensionality of the input space by incorporating temporal information at the output of the network, instead of as a temporal window at the input of the net. Application of Rho-Nets to chemical process simulation is discussed.<> View full abstract»

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  • Qualitative interpretation of sensor patterns

    Page(s): 54 - 63
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    A framework that provides the ability to generate qualitative interpretations (QIs) from multisensor trend patterns for monitoring, control, and optimization of chemical plants is presented. QIs are transformations of sensor and quality product data into useful symbolic abstractions. The framework is founded on the principles of similarity-based pattern recognition. Although demonstrated for normality identification, the machine methodology is general-purpose and applicable to any context-dependent QI problem. The objective of this approach is to create a QI-map of known pattern classes that consists of spatially distinguishable regions of patterns in an n-dimensional representation space. Creation of a QI-map is a two-step process: unsupervised map generation followed by supervised labeling. The application of the ART2 neural network for clustering in the QI-map is described. The application of the ART2-based QI-map approach to process monitoring of a recycle reactor is also described.<> View full abstract»

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  • Functional representation and reasoning about the F/A-18 aircraft fuel system

    Page(s): 65 - 71
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    Functional reasoning, a subfield of model-based reasoning, is discussed. This approach uses abstractions of a device's purpose to index behaviors that achieve that purpose. Functional modeling, a variation on this method, also uses simulation as a core reasoning strategy. The complex causal knowledge of a device along functional lines is decomposed, then a causal story of how the device will operate in a particular situation given stated boundary conditions is composed. The application of the functional approach to modeling the fuel system of a F/A-18 aircraft is described. The representation of the F/A-18 fuel system includes 89 component devices, 92 functions, 118 behaviors, and 181 state variables.<> View full abstract»

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  • The reliability of embedded AI systems

    Page(s): 72 - 78
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (953 KB)  

    An artificial intelligence (AI) software reliability model, which accounts for the trade-off between correctness and response time, fuzzy rather than binary output, faults in fundamental techniques used in AI programs, and the evolving database that AI programs rely on, is discussed. The model can be reduced to a simpler form for most applications, such as stand-alone expert systems and game-playing programs. In its full generality, it is applicable to real-time planning systems involving hardware and software.<> View full abstract»

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  • Net-based computational models of knowledge-processing systems

    Page(s): 79 - 86
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    Net representation, which provides a uniform semantics to a wide class of computational problems, such as numeric functions, logical systems, and models of dynamic and distributed systems, is reviewed. Using a uniform formalism of colored propositional nets, the methodology of net models is extended to represent Horn clauses, non-Horn clauses, and expert systems. It is shown that, using this formalism, tools can be built for expanding and partitioning knowledge-bases, and testing knowledge-base properties such as consistency, redundancy, and deadlocks.<> View full abstract»

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Aims & Scope

This Periodical ceased production in 1997. The current retitled publication is IEEE Intelligent Systems.

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