Advanced Search
CS Search Google Search
Subscribers, please login

Published Articles >> Table of Contents >> Abstract

12th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'00)   p. 0186
Tools for intelligent decision support system development in the legal domain

Full Article Text: Download PDF of full textBuy this articleGet full text from IEEE Xplore

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TAI.2000.889867
Send link to a friend

Abstract
Abstract: We describe tools that are particularly suited to the development of knowledge based systems in the domain of law. The first tool is a conceptual model that supports the categorization of legal tasks. Once categorized, tasks can be mapped onto to the most appropriate artificial reasoning model (if any) for computer based implementation. This scheme is based on theoretical and cultural perspectives that have currency in the legal domain yet may have less relevance in other domains. The second tool, which we call the sequenced transition network, involves reducing the involvement of a knowledge engineer so that domain experts can more easily develop and maintain their own knowledge bases.
Additional Information
Index Terms- law administration; knowledge based systems; decision support systems; case-based reasoning; intelligent decision support system development tools; legal domain; conceptual model; legal task categorization; law; artificial reasoning model; cultural perspectives; sequenced transition network; knowledge engineer; domain experts; knowledge bases

Citation:  A. Stranieri, J. Zeleznikow, "Tools for intelligent decision support system development in the legal domain," ictai, p. 0186,  12th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'00),  2000

Similar Articles

Abstract Contents
Abstract
Index Terms
Citation




Free access to

  • Abstracts
  • Selected PDFs

Electronic subscribers login to:

  • Access HTML/PDFs of full text articles

Subscription information

Get a Web account

Peer Review Notice

Give us Feedback