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The inference process in a probabilistic and conditional environmentunder minimum relative entropy, permits the acquisition of basicknowledge, the consideration of - even uncertain - ad hoc knowledge,and the response to queries. Even if these procedures are wellknown in the relevant literature their realisation for large-scaleapplications needs a sophisticated tool, allowing the communicationwith the user as well as all relevant logical transformationsand numerical calculations. SPIRIT is an Expert-System-Shellfor these purposes. Even for hundreds of – consistent– facts about the involved variables’ dependenciesthe shell automatically generates the corresponding epistemicstate, thus permitting the derivation of conclusions from theacquired knowledge. These conclusions’ reliability orprecision can be checked, inviting the user to enrich the knowledgeby further facts, if desired. Any inconsistencies among providedfacts are detected, and their elimination will be supportedby the shell. Knowledge acquisition can come from provided factsby a knowledge engineer as well as from real world data; inductivelearning supports the use of such data. An important capabilityof the shell is the calculation of impacts upon ideas or conceptsfrom a given stimulus. This paper is a brief survey of theoreticalconcepts and the corresponding features of the system, whichare accompanied by illustrative examples.  相似文献   
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