A Table-Based Representation for Probabilistic Logic: Preliminary Results

TL;DR

This paper introduces pDMN, a probabilistic extension of DMN, enabling probabilistic reasoning within decision models while maintaining user-friendliness, and translating models into ProbLog for flexible reasoning.

Contribution

It presents pDMN, a novel probabilistic decision modeling notation that extends DMN with probabilistic features and can be translated into ProbLog for reasoning.

Findings

pDMN successfully models probabilistic decision logic.

Models can be translated into ProbLog for reasoning.

Preliminary results demonstrate feasibility.

Abstract

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.