著者

萩原 信吾 東条 敏

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.24, no.5, pp.405-416, 2009 (Released:2009-06-30)

参考文献数

34

被引用文献数

1

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In this paper, we propose a verification methodology of large-scale legal knowledge. With a revision of legal code, we are forced to revise also other affected code to keep the consistency of law. Thus, our task is to revise the affected area properly and to investigate its adequacy. In this study, we extend the notion of inconsistency besides of the ordinary logical inconsistency, to include the conceptual conflicts. We obtain these conflictions from taxonomy data, and thus, we can avoid tedious manual declarations of opponent words. In the verification process, we adopt extended disjunctive logic programming (EDLP) to tolerate multiple consequences for a given set of antecedents. In addition, we employ abductive logic programming (ALP) regarding the situations to which the rules are applied as premises. Also, we restrict a legal knowledge-base to acyclic program to avoid the circulation of definitions, to justify the relevance of verdicts. Therefore, detecting cyclic parts of legal knowledge would be one of our objectives. The system is composed of two subsystems; we implement the preprocessor in Ruby to facilitate string manipulation, and the verifier in Prolog to exert the logical inference. Also, we employ XML format in the system to retain readability. In this study, we verify actual code of ordinances of Toyama prefecture, and show the experimental results.

著者

東条 敏 萩原 信吾

出版者

JAIST Press

巻号頁・発行日

vol.3, 2008-06

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In 2002, Toyama Prefecture in Japan had a change of policy allowing residents to submit various kinds of forms electrically, in accordance with the development of the Internet. At that time, many municipal officers were forced to rewrite regional ordinances by hand. Legal codes are intrinsically destined to be modified and revised in later years, to catch up with the requirements of our society. However, with each revision the coherence of the code is threatened, and in worse cases it may contain discordance and inconsistency in itself. In many research on legal reasoning, researchers often regard that the code is always consistent though they may sometimes need to add incomplete knowledge to get beneficial consequences. However, when a new legislation, jurists need to inspect rigidly whether it is coherent with existing one. In this revision procedure, jurists must assess how the affected area is large. If (s)he finds discordance with a new legislation, and (s)he modifies it, then (s)he needs to search for the affected area further from the newly revised law recursively. Thus, such a revision would be a tedious and painstaking work. Our motivation in this study is to identify the affected area automatically and to detect discordance in a practical code.