2013/03/27 by Lashon B. Booker, Booker, Lashon B., Naveen Hota +1
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Rough Sets and Fuzzy Logic #cs.AI
paper · pdf · doi:10.48550/arxiv.1304.3078
Appears in Proceedings of the Second Conference on Uncertainty in Artificial Intelligence (UAI1986)
arxiv created 2013/03/27 · openalex publication_date 2013/03/27 · arxiv updated 2013/04/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
One of the most important aspects of current expert systems technology is the ability to make causal inferences about the impact of new evidence. When the domain knowledge and problem knowledge are uncertain and incomplete Bayesian reasoning has proven to be an effective way of forming such inferences [3,4,8]. While several reasoning schemes have been developed based on Bayes Rule, there has been very little work examining the comparative effectiveness of these schemes in a real application. This paper describes a knowledge based system for ship classification [1], originally developed using the PROSPECTOR updating method [2], that has been reimplemented to use the inference procedure developed by Pearl and Kim [4,5]. We discuss our reasons for making this change, the implementation of the new inference engine, and the comparative performance of the two versions of the system.