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  • Wiley  (5)
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  • Wiley  (5)
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  • 1
    Online Resource
    Online Resource
    Incrementally updating a hybrid rule base based on empirical data
    Wiley ; 2007
    In:  Expert Systems Vol. 24, No. 4 ( 2007-09), p. 212-231
    Details
    In: Expert Systems, Wiley, Vol. 24, No. 4 ( 2007-09), p. 212-231
    Abstract: Abstract: Neurules are a kind of hybrid rules that combine a symbolic (production rules) and a connectionist (adaline unit) representation. One way that the neurules can be produced is from training examples/patterns, extracted from empirical data. However, in certain application fields not all of the training examples are available a priori. A number of them become available over time. In those cases, updating the neurule base is necessary. In this paper, methods for updating a hybrid rule base, consisting of neurules, to reflect the availability of new training examples are presented. They can be considered as a type of incremental learning method that retains the entire induced hypothesis and all past training examples. The methods are efficient, since they require the least possible retraining effort and the number of neurules produced is kept as small as possible. Experimental results that prove the above argument are presented.
    Type of Medium: Online Resource
    ISSN: 0266-4720 , 1468-0394
    URL: Issue
    URL: Article
    DOI: 10.1111/exsy.2007.24.issue-4
    DOI: 10.1111/j.1468-0394.2007.00430.x
    Language: English
    Publisher: Wiley
    Publication Date: 2007
    detail.hit.zdb_id: 283676-2
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  • 2
    Online Resource
    Online Resource
    Categorizing approaches combining rule‐based and case‐based reasoning
    Wiley ; 2007
    In:  Expert Systems Vol. 24, No. 2 ( 2007-05), p. 97-122
    Details
    In: Expert Systems, Wiley, Vol. 24, No. 2 ( 2007-05), p. 97-122
    Abstract: Abstract: Rule‐based and case‐based reasoning are two popular approaches used in intelligent systems. Rules usually represent general knowledge, whereas cases encompass knowledge accumulated from specific (specialized) situations. Each approach has advantages and disadvantages, which are proved to be complementary to a large degree. So, it is well justified to combine rules and cases to produce effective hybrid approaches, surpassing the disadvantages of each component method. In this paper, we first present advantages and disadvantages of rule‐based and case‐based reasoning and show that they are complementary. We then discuss the deficiencies of existing categorization schemes for integrations of rule‐based and case‐based representations. To deal with these deficiencies, we introduce a new categorization scheme. Finally, we briefly present representative approaches for the final categories of our scheme.
    Type of Medium: Online Resource
    ISSN: 0266-4720 , 1468-0394
    URL: Issue
    URL: Article
    DOI: 10.1111/exsy.2007.24.issue-2
    DOI: 10.1111/j.1468-0394.2007.00423.x
    Language: English
    Publisher: Wiley
    Publication Date: 2007
    detail.hit.zdb_id: 283676-2
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  • 3
    Online Resource
    Online Resource
    Improving efficiency of merging symbolic rules into integrated rules: splitting methods and mergability criteria
    Wiley ; 2015
    In:  Expert Systems Vol. 32, No. 2 ( 2015-04), p. 244-260
    Details
    In: Expert Systems, Wiley, Vol. 32, No. 2 ( 2015-04), p. 244-260
    Abstract: Neurules are a type of neuro‐symbolic rules integrating neurocomputing and production rules. Each neurule is represented as an adaline unit. Neurules exhibit characteristics such as modularity, naturalness and ability to perform interactive and integrated inferences and provide explanations for reached conclusions. One way of producing a neurule base is through conversion of an existing symbolic rule base yielding an equivalent but more compact rule base. The conversion process merges symbolic rules having the same conclusion into one or more neurules. Because of the inability of the adaline unit to handle inseparability, more than one neurule for each conclusion may be produced by splitting the initial set of symbolic rules into subsets. This paper presents research work improving the conversion process in terms of runtime and number of produced neurules. First, we show how easier it is to construct a neurule base than a connectionist one. Second, we present alternative rule set splitting methods. Finally, we define criteria concerning the ability or inability to convert a rule set into a single, equivalent, but more compact rule. With application of such mergability criteria, the conversion process of symbolic rules into neurules becomes more time‐efficient. All the aforementioned are supported by experimental results.
    Type of Medium: Online Resource
    ISSN: 0266-4720 , 1468-0394
    URL: Issue
    URL: Article
    DOI: 10.1111/exsy.v32.2
    DOI: 10.1111/exsy.12085
    Language: English
    Publisher: Wiley
    Publication Date: 2015
    detail.hit.zdb_id: 283676-2
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  • 4
    Online Resource
    Online Resource
    Assessment of life insurance applications: an approach integrating neuro‐symbolic rule‐based with case‐based reasoning
    Wiley ; 2016
    In:  Expert Systems Vol. 33, No. 2 ( 2016-04), p. 145-160
    Details
    In: Expert Systems, Wiley, Vol. 33, No. 2 ( 2016-04), p. 145-160
    Abstract: Assessment of applications for life insurance is an important task in the insurance sector that concerns estimation of potential risks underlying an application, if accepted. This task is accomplished by specialized personnel of insurance companies. Because of recent financial crises, this task is more demanding, and intelligent computer‐based methods could be employed to assist. In this paper, we present an intelligent approach to assessment of life insurance applications, which is based on an integration of neurule‐based with case‐based reasoning. Neurules are a type of neuro‐symbolic rules that combine a symbolic (production rules) and a connectionist (adaline unit) representation. A characteristic of neurules is that in contrast to other hybrid neuro‐symbolic approaches, they retain the naturalness and modularity of symbolic rules. Neurules are produced from available symbolic rules that represent general knowledge, which however do not completely cover the domain. We use health condition, age, gender, annual income, profession, insurance type and primary life insurance benefit as assessment parameters used in rule conditions. The integration of neurules and cases employs different types of indices for the cases according to different roles they play in neurule‐based reasoning. This results in its accuracy improvement. Experimental results demonstrate the effectiveness of the approach.
    Type of Medium: Online Resource
    ISSN: 0266-4720 , 1468-0394
    URL: Issue
    URL: Article
    DOI: 10.1111/exsy.v33.2
    DOI: 10.1111/exsy.12137
    Language: English
    Publisher: Wiley
    Publication Date: 2016
    detail.hit.zdb_id: 283676-2
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  • 5
    Online Resource
    Online Resource
    Automatic estimation of exercises ' difficulty levels in a tutoring system for teaching the conversion of natural language into first‐order logic
    Wiley ; 2016
    In:  Expert Systems Vol. 33, No. 6 ( 2016-12), p. 569-580
    Details
    In: Expert Systems, Wiley, Vol. 33, No. 6 ( 2016-12), p. 569-580
    Abstract: The estimation of the difficulty level of exercises is a fundamental aspect of intelligent tutoring systems, and it is necessary in order to achieve better adaptation to the students' needs and maximize learning efficiency. In this article, we present an approach to automatically estimates the difficulty level of exercises in natural language (NL) to first‐order of logic (FOL). The estimation of an exercise's difficulty level is based on the complexity of the corresponding answer, that is the FOL formula, as well as the structure and the semantics of the exercise, that is a natural language sentence and it is carried out in two main steps. Initially, a preliminary estimation is performed based on the complexity of the FOL formula. The system takes as input parameters the number, the type and the order of quantifiers, the number of implications, and the number of different connectives. Afterwards, the final estimation is made based on both semantic aspects of the NL sentence and the structure of the FOL formula. An evaluation study was conducted to assess the system's performance, and the results are very encouraging.
    Type of Medium: Online Resource
    ISSN: 0266-4720 , 1468-0394
    URL: Issue
    URL: Article
    DOI: 10.1111/exsy.v33.6
    DOI: 10.1111/exsy.12182
    Language: English
    Publisher: Wiley
    Publication Date: 2016
    detail.hit.zdb_id: 283676-2
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