Experiments with Probabilistic Consultation Systems

This paper describes simulation experiments with probabilistic consultation systems, also called expert systems. The aim was to test the relative performance of systems with inference algorithms based on different assumptions. The approach used was simulation of task generation, knowledge acquisition, and task solving. The main results found were that a rule value approach seems to be superior to a goal driven approach, that the use of the complete Bayes’ formula improves the the quality of the task solutions, that ignorance of statistical dependence among antecedents makes the estimated probability of the predictions useless as a reliability indicator, but that models taking the dependencies into account can be designed.