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20112013
most citedIncremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes

345 citations · 643 across the 16 of their papers we have counts for

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16 papers · 1 filter

cs.AI2013

Sidestepping the Triangulation Problem in Bayesian Net Computations

Nevin Lianwen Zhang, David L. Poole

This paper presents a new approach for computing posterior probabilities in Bayesian nets, which sidesteps the triangulation problem. The current state of art is the clique tree pr…

cs.AI2013

Incremental computation of the value of perfect information in stepwise-decomposable influence diagrams

Nevin Lianwen Zhang, Runping Qi, David L. Poole

To determine the value of perfect information in an influence diagram, one needs first to modify the diagram to reflect the change in information availability, and then to compute…

cs.AI2013

Inter-causal Independence and Heterogeneous Factorization

Nevin Lianwen Zhang, David L Poole

It is well known that conditional independence can be used to factorize a joint probability into a multiplication of conditional probabilities. This paper proposes a constructive d…

cs.AI2013

Solving Asymmetric Decision Problems with Influence Diagrams

Runping Qi, Nevin Lianwen Zhang, David L. Poole

While influence diagrams have many advantages as a representation framework for Bayesian decision problems, they have a serious drawback in handling asymmetric decision problems. T…

cs.AI20133 cited

Inference with Causal Independence in the CPSC Network

Nevin Lianwen Zhang

This paper reports experiments with the causal independence inference algorithm proposed by Zhang and Poole (1994b) on the CPSC network created by Pradhan et al. (1994). It is foun…

cs.AI20132 cited

Fast Value Iteration for Goal-Directed Markov Decision Processes

Nevin Lianwen Zhang, Weihong Zhang

Planning problems where effects of actions are non-deterministic can be modeled as Markov decision processes. Planning problems are usually goal-directed. This paper proposes sever…