A Table-Based Representation for Probabilistic Logic: Preliminary Results
This work provides a user-friendly probabilistic logic representation for domain experts, though it appears incremental as it builds directly on existing DMN and ProbLog frameworks.
The authors tackled the problem of extending deterministic decision logic to probabilistic reasoning by introducing pDMN, a probabilistic extension of DMN that includes predicates, functions, quantification, and a new hit policy, with models translatable into ProbLog programs for query answering.
We present Probabilistic Decision Model and Notation (pDMN), a probabilistic extension of Decision Model and Notation (DMN). DMN is a modeling notation for deterministic decision logic, which intends to be user-friendly and low in complexity. pDMN extends DMN with probabilistic reasoning, predicates, functions, quantification, and a new hit policy. At the same time, it aims to retain DMN's user-friendliness to allow its usage by domain experts without the help of IT staff. pDMN models can be unambiguously translated into ProbLog programs to answer user queries. ProbLog is a probabilistic extension of Prolog flexibly enough to model and reason over any pDMN model.