Evaluation of Uncertain Inference Models I: PROSPECTOR
arXiv:1304.3117
Abstract
This paper examines the accuracy of the PROSPECTOR model for uncertain reasoning. PROSPECTOR's solutions for a large number of computer-generated inference networks were compared to those obtained from probability theory and minimum cross-entropy calculations. PROSPECTOR's answers were generally accurate for a restricted subset of problems that are consistent with its assumptions. However, even within this subset, we identified conditions under which PROSPECTOR's performance deteriorates.
Appears in Proceedings of the Second Conference on Uncertainty in Artificial Intelligence (UAI1986)