Eyal Shlomo Shimony

We evaluate current explanation schemes. These are either insufficiently general, or suffer from other serious drawbacks. A domain-independent explanation theory, based on ignoring irrelevant variables in a probabilistic setting, is proposed. Independence-based maximum aposteriori probability (IB-MAP) explanations, an instance of irrelevance-based explanation, has several interesting properties, which provide for simple algorithms for computing such explanations. A best-first algorithm that generates IB-MAP explanations is presented, and evaluated empirically. The algorithm shows reasonable performance for up to medium-size problems on a set of randomly generated belief networks. An alternate algorithm, based on linear systems of inequalities, is discussed.