A quantitative analysis of reasoning for RMSes

Laurent Buisson (1) , Jérôme Euzenat (1)

1 SHERPA - Object Knowledge Bases

For reasoning systems, it is sometime useful to cache away the inferred values. Meanwhile, when the system works in a dynamic environment, cache coherence has to be performed, and this can be achieved with the help of a reasoning maintenance system (RMS). The questions to be answered, before implementing such a system for a particular application, are: how much is caching useful ? Does the system need a dynamicity management system ? Is a RMS suited (what will be its overhead) ? We provide an application driven evaluation framework in order to answer these questions. The evaluation is based on the real work to be processed on the reasoning of the application. First, we express the action of caching and maintaining with two concepts: backward and forward cone effects. Then we quantify the inference time for those systems and find the quantification of the cone effects in the formulas.

Domaines: Intelligence artificielle [cs.AI]

Laurent Buisson, Jérôme Euzenat. A quantitative analysis of reasoning for RMSes. 6th International Symposium on Methodologies for Intelligent Systems (ISMIS), Oct 1991, Charlotte, United States. pp.9-20. ⟨hal-01401195⟩