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Title: KICS : representation of regulatory information and the use of case-based reasoning to support the relaxation process
Author: Yang, Soon-Ae
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 1995
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This thesis presents a case-based reasoning system (KICS) which can assist domain experts in interpreting building regulations in relaxation cases. In traditional legal decision support systems, it has been regarded as natural to represent legal rules in statutes in terms of If-Then decision rules and to link these rules to a separate case-based reasoning system for handling cases. However, we take a view that legal rules in the statutory regulations are the results of accumulation and generalisation of rulings made in case histories and this has led us to a unified case-based approach to handle both statutory regulations and cases. First, we propose a unified case-based model of regulatory information. In this model, regulatory information, i.e., legal rules from statutes and precedent cases, are represented as models, and interpretation hierarchies of legal rules are represented as abstraction hierarchies of models in the Model Knowledge Base. Actual cases are stored in the case Library together with arguments debated. Background domain knowledge used in classifying input cases are represented as semantic networks and heuristic rules in the Domain Knowledge Base. Second, we propose to use case-based reasoning to access and maintain regulatory information. Models relevant to the input case are retrieved by identifying the level of abstraction at which the input case is described and by selecting models similar to the input case. If the input case is not compliant with the retrieved models, principles behind retrieved legal rules and previous similar cases are explained to the user and ask whether relaxation can be granted. Decision on relaxation is made by the user, and rulings made in cases in which relaxation is granted are acquired by generalising them and (if possible) combining them with existing models in the abstraction hierarchies.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available