Knowledge Representation for an Uncertain World.
Abstract : Any application where an intelligent agent interacts with the real world must deal with the problem of uncertainty. Bayesian belief networks have become dominant in addressing this issue. This is a framework based on principled probabilistic semantics, which achieves effective knowledge representation and inference capabilities by utilizing the locality structure in the domain: typically, only very few aspects of the situation directly affect each other. Despite their success, belief networks are inadequate as a knowledge representation language for large, complex domains: Their attribute-based nature does not allow us to express general rules that hold in many different circumstances. This prevents knowledge from being shared among applications; the initial knowledge acquisition cost has to be paid for each new domain. It also inhibits the construction of large complex networks. We deal with this issue by presenting a rich knowledge-representation language from which belief networks can be constructed to suit specific circumstances, algorithms for learning the network parameters from data, fast approximate inference algorithms designed to deal with the large networks that result. We show how these techniques can be applied in domains involving continuous variables, in situations where the world changes over time, and in the context of planing under uncertainty.