2013/03/27 by John P. Fox, Fox, John
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.1304.3448
openalex publication_date 2013/03/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The last few years has seen a growing debate about techniques for managing uncertainty in AI systems. Unfortunately this debate has been cast as a rivalry between AI methods and classical probability based ones. Three arguments for extending the probability framework of uncertainty are presented, none of which imply a challenge to classical methods. These are (1) explicit representation of several types of uncertainty, specifically possibility and plausibility, as well as probability, (2) the use of weak methods for uncertainty management in problems which are poorly defined, and (3) symbolic representation of different uncertainty calculi and methods for choosing between them.