
Towards Solving the Multiple Extension Problem: Combining Defaults and Probabilities
The multiple extension problem arises frequently in diagnostic and defau...
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Probabilistic Semantics and Defaults
There is much interest in providing probabilistic semantics for defaults...
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Can Uncertainty Management be Realized in a Finite Totally Ordered Probability Algebra?
In this paper, the feasibility of using finite totally ordered probabili...
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A Dynamic Approach to Probabilistic Inference
In this paper we present a framework for dynamically constructing Bayesi...
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What is an Optimal Diagnosis?
Within diagnostic reasoning there have been a number of proposed definit...
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High Level Path Planning with Uncertainty
For high level path planning, environments are usually modeled as distan...
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Representing Bayesian Networks within Probabilistic Horn Abduction
This paper presents a simple framework for Horn clause abduction, with p...
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Sidestepping the Triangulation Problem in Bayesian Net Computations
This paper presents a new approach for computing posterior probabilities...
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Exploring Localization in Bayesian Networks for Large Expert Systems
Current Bayesian net representations do not consider structure in the do...
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Incremental computation of the value of perfect information in stepwisedecomposable influence diagrams
To determine the value of perfect information in an influence diagram, o...
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The use of conflicts in searching Bayesian networks
This paper discusses how conflicts (as used by the consistencybased dia...
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Intercausal Independence and Heterogeneous Factorization
It is well known that conditional independence can be used to factorize ...
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Solving Asymmetric Decision Problems with Influence Diagrams
While influence diagrams have many advantages as a representation framew...
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A Framework for DecisionTheoretic Planning I: Combining the Situation Calculus, Conditional Plans, Probability and Utility
This paper shows how we can combine logical representations of actions a...
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Flexible Policy Construction by Information Refinement
We report on work towards flexible algorithms for solving decision probl...
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ContextSpecific Approximation in Probabilistic Inference
There is evidence that the numbers in probabilistic inference don't real...
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An Anytime Algorithm for Decision Making under Uncertainty
We present an anytime algorithm which computes policies for decision pro...
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Estimating the Value of Computation in Flexible Information Refinement
We outline a method to estimate the value of computation for a flexible ...
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Reasoning With Conditional Ceteris Paribus Preference Statem
In many domains it is desirable to assess the preferences of users in a ...
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Building a Stochastic Dynamic Model of Application Use
Many intelligent user interfaces employ application and user models to d...
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Efficient Inference in Large Discrete Domains
In this paper we examine the problem of inference in Bayesian Networks w...
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Nonparametric Bayesian Logic
The Bayesian Logic (BLOG) language was recently developed for defining f...
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Constraint Processing in Lifted Probabilistic Inference
Firstorder probabilistic models combine representational power of first...
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Seeing the Forest Despite the Trees: Large Scale SpatialTemporal Decision Making
We introduce a challenging realworld planning problem where actions mus...
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David L Poole
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Professor of Computer Science at University of British Columbia