Tackling the DM Challenges with cDMN: A Tight Integration of DMN and Constraint Reasoning

SIMON VANDEVELDE; BRAM AERTS; JOOST VENNEKENS

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Tackling the DM Challenges with cDMN: A Tight Integration of DMN and Constraint Reasoning

机译：Tackling the DM Challenges with cDMN: A Tight Integration of DMN and Constraint Reasoning

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Knowledge-based AI typically depends on a knowledge engineer to construct a formal model ofdomain knowledge – but what if domain experts could do this themselves? This paper describesan extension to the Decision Model and Notation (DMN) standard, called Constraint DecisionModel 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 domainknowledge, while retaining DMNs goal of being understandable by domain experts. We testcDMN by solving the most complex challenges posted on the DM Community website. Wecompare our own cDMN solutions to the solutions that have been submitted to the website andfind that our approach is competitive. Moreover, cDMN is able to solve more challenges thanany other approach.

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《Theory and practice of logic programming》 |2023年第3期|535-558|共24页
作者
SIMON VANDEVELDE; BRAM AERTS; JOOST VENNEKENS;
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KU Leuven, De Nayer Campus, Department of Computer ScienceJ.-P. De Nayerlaan 5, 2860 Sint-Katelijne-Waver, BelgiumLeuven.AI - KU Leuven Institute for AI, B-3000 Leuven, Belgium;

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decision model and notation; constraint reasoning; expressiveness; readability; IDP system;

Tackling the DM Challenges with cDMN: A Tight Integration of DMN and Constraint Reasoning

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