Abstract
Current probabilistic expert systems assume complete knowledge of the joint distribution. To specify this distribution one has to construct a directed acyclic graph attached by a lot of tables filled with conditional probabilities. Often these probabilities are unknown and the quantification is more or less arbitrary. SPIRIT is an expert system shell for probabilistic knowledge bases which uses the principle of maximum entropy to avoid these lacks. Knowledge acquisition is performed by specifying probabilistic facts and rules on discrete variables in an extended propositional logic syntax. The shell generates the unique probability distribution which respects all facts and rules and maximizes entropy. After creating this distribution the shell is ready for answering simple and complex queries. The process of knowledge acquisition, knowledge processing and answering queries is revealed in detail on a nontrivial example.
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