Identifying the most effective reasoning calculi for a knowledge-based system

An experiment was conducted that used a knowledge-based system to classify a set of natural language documents. An important issue in the design of knowledge-based systems is how to equip them with the capability to transmit uncertainty from imprecise premises to a conclusion and associate the conclusion with a level of belief. A study was carried out to compare the effectiveness of various fuzzy calculi. The test domain was full-text retrieval of natural language documents. This test domain was chosen because natural language is inherently imprecise and there are well-defined performance measures that can provide the basis of calculi comparison. Both robustness and relative retrieval quality were studied. Some calculi were found to be more effective. The most commonly used knowledge-based systems calculi were not the best performing