Abstract
Abstract
Knowledge-based AI typically depends on a knowledge engineer to construct a formal model of domain knowledge – but what if domain experts could do this themselves? This paper describes an extension to the Decision Model and Notation (DMN) standard, called Constraint Decision Model and Notation (cDMN). DMN is a user-friendly, table-based notation for decision logic, which allows domain experts to model simple decision procedures without the help of IT staff. cDMN aims to enlarge the expressiveness of DMN in order to model more complex domain knowledge, while retaining DMNs goal of being understandable by domain experts. We test cDMN by solving the most complex challenges posted on the DM Community website. We compare our own cDMN solutions to the solutions that have been submitted to the website and find that our approach is competitive. Moreover, cDMN is able to solve more challenges than any other approach.
Publisher
Cambridge University Press (CUP)
Subject
Artificial Intelligence,Computational Theory and Mathematics,Hardware and Architecture,Theoretical Computer Science,Software
Cited by
3 articles.
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1. Proceedings 39th International Conference on Logic Programming;Electronic Proceedings in Theoretical Computer Science;2023-09-12
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3. Knowledge-Based Support for Adhesive Selection;Logic Programming and Nonmonotonic Reasoning;2022