Early Work (1992-2002)
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) . |
,
N. L. Zhang
and D. Poole (1996), Exploiting causal
independence in Bayesian network inference,Journal of
Artificial Intelligence Research, 5: 301-328. [Google citation count: 495] ,
N. L. Zhang
and D. Poole (1994), A simple approach to
Bayesian network computations, in Proc. of the 10th Canadian
Conference on Artificial Intelligence, Banff, Alberta, Canada, May 16-22.
[Google citation count: 336] (Featured at BN-wiki
) ,
D. Poole and Nevin L. Zhang
(2003). Exploiting contextual
independence in probabilistic inference. Journal of Artificial
Intelligence Research, 18: 263-313. [Google citation count:
122] |
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. |
,
Anthony Cassandra, Michael L.
Littman, and N. L. Zhang (1997), Incremental Pruning: A
Simple, Fast, Exact Algorithm for Partially Observable Markov Decision
Processes in Proc. of the 13th Conference on Uncertainties in
Artificial Intelligence. [Google citation count: 485] ,
N. L. Zhang and
W. Liu (1997), A model approximation scheme
for planning in partially observable stochastic domains, Journal of
Artificial Intelligence Research, 7: 199-230. [Google citation
count: 85] ,
N. L. Zhang and
W. Liu (1996), Planning in stochastic domains: Problem
characteristics and approximation.
Technical Report HKUST-CS96-31 [Google citation count: 97 ,
Zhang, N.L.
and Zhang, W. (2001) "Speeding up the Convergence
of Value Iteration in Partially Observable Markov Decision Processes",
Journal of Artificial Intelligence Research, 14: 29-51. [Google
citation count: 149] |
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) . |
,
N. L. Zhang
(1998), Probabilistic Inference in
Influence Diagrams, Computational Intelligence ,
14(4): 475-497. [Google citation count: 117] ,
N. L. Zhang
R. Qi and D. Poole (1994) A computational theory of decision networks, International
Journal of Approxi mate Reasoning, 1994, 11 (2): 83-158. [Google
citation count: 78] |