Efficient Global Probabilistic Deduction from Taxonomic and Probabilistic Knowledge-Bases over Conjunctive Events

We present a new, efficient linear programming approach to probabilistic deduction from probabilistic knowledge-bases over conjunctive events. We show that this approach enables us to solve the classical problem of probabilistic deduction along a chain of basic events in polynomial time in the length of the chain. We then elaborate how taxonomic knowledge can be exploited in our new approach for an increased efficiency. We also present important new results for the classical linear programming approach to probabilistic deduction under taxonomic knowledge. 1 Introduction There are many approaches to non-Bayesian probabilistic deduction in the literature. They can be classified in global techniques based on linear programming and in local methods founded on the iterative application of inference rules. Non-Bayesian probabilistic deduction by solving linear programs is discussed e.g. in [23], [13], [24], [17], [14], [2], [15], and [22]. It can be performed within rich probabilistic lang..