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Extracting knowledge from examples: Induction of heuristic rules for wheelchair prescription

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Abstract

The feasibility of using an induction algorithm to discover heuristic rules for wheelchair equipment selection is investigated. Syntactical rules for two description languages (one to describe the disabled client and another to describe wheelchair equipment configurations) are presented. These languages allow the rulebase developer to describe training instances (examples) to the computer in an intelligible way. An induction learning algorithm is used to classify these training instances, thereby producing a decision tree. Heuristic rules can then be extracted from the tree and placed in a rulebase for an expert system called LEADER. LEADER is being developed to aid a wheelchair prescriber in the equipment selection process. There are two important objectives of this research: (1) to reduce the time and facilitate the development of an intelligent expert system rulebase by extracting knowledge embedded within existing examples and (2) to provide the expert system with the ability to learn new rules autonomously. The ability to learn makes the rulebase dynamic; the initial rulebase would be augmented with new rules as additional examples are provided to the system while it is in clinical use.

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Authors and Affiliations

  1. Department of Industrial Engineering, and Information Systems, Northeastern University, Boston, MA, USA

    Sean M. Callahan & Gerard Voland

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  1. Sean M. Callahan
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  2. Gerard Voland
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Callahan, S.M., Voland, G. Extracting knowledge from examples: Induction of heuristic rules for wheelchair prescription. J Intell Robot Syst 14, 133–153 (1995). https://doi.org/10.1007/BF01559609

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  • DOI: https://doi.org/10.1007/BF01559609

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