Probabilistic Reasoning in Knowledge Based Systems
Knowledge based systems, which started with purely deterministic rule processing in the early 70's, are nowadays able to manage uncertain, subjective and vague knowledge. One research area considers probabilistic facts and rules a suitable communication tool between the user and the system, since probabilities are particularly suitable to quantify given dependencies formulated by experts. The probabilities of such facts and rules must be either estimated by an expert or calculated from statistical data.
One of the most sophisticated expert system shells is SPIRIT, which allows an easy but rich communication with the user. SPIRIT stands for Symmetrical Probabilistic Intensional Reasoning in Inference Networks in Transition. You may use subjective estimations of probabilities in a knowledge domain as well as statistical data to construct a knowledge base. The construction is performed in an optimal way allowing partial information about the domain, too. This ability makes it one of the most modern shells.