Early Work (1992-2002)
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Probabilistic Reasoning: Our most influential work in this area is an inference algorithm for Bayesian networks (BN) called variable elimination (Zhang and Poole 1994, 1996). It is the first inference algorithm for BN discussed in an popular BN textbook by Koller and Friedman and another popular AI textbook by Russell and Norvig, and the first inference algorithm for BN discussed in related courses offered at many universities. We have also proposed methods for exploiting causal independence and contextual independence in Bayesian network inference (Zhang and Poole 1996, 1999, and Poole and Zhang 2003) . |
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Decision-Theoretic Planning with POMDPs: We have two notable results in this area: an exact algorithm called incremental pruning (IP) (Zhang and Liu 1997, Cassandra et al. 1997) and an approximate algorithm called pointed-based value iteration (PBVI) (Zhang and Zhang 2001). IP is fundamental to the theory of POMDPs, while PBVI is a the key to make POMDPs practical. A large number of papers on PBVI were published subsequent to our work. |
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Decision under Uncertainty: In this area, we proposed a general framework for representing and solving decision problems (Zhang et al 1994), and showed how general Bayesian network inference algorithm can be utilized to find optimal decisions in the framework (Zhang 1998) . |
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