Reasoning about the disruption patterns for train system using Bayesian Network and Prolog
Yunita Rachma Pradiawati, Yanti Rusmawati, Muhammad Arzaki · Journal of Physics Conference Series · 2019
We construct a Prolog-based expert system to reason about the disruption patterns for train system using Bayesian network and Prolog. The disruptions dependencies are modeled using Bayesian network and the reasoning is carried on using Prolog. We choose Bayesian network because it is one of the most efficient and elegant framework to represent and reason probabilistic graphical model. The causative relationship among disruptions is represented using Directed Acyclic Graph (DAG). We use Prolog to improve the efficiency of the reasoning process by defining Bayesian network and its probabilistic information into a knowledge base. The causative relationships among disruptions are also modeled in terms of Prolog rules. Our Prolog-based expert system combines the statistical reasoning capability of Bayesian network and logic programming efficiency. The system provides comprehensive reasoning regarding the causative probability of events, the causative relationship among disruptions, as well as the most triggering and triggered disruptions in train system.