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    UAI
    2004
    92views Artificial Intelligence» more  UAI 2004»
    Hybrid Influence Diagrams Using Mixtures of Truncated Exponentials
    13 years 6 months ago
    Hybrid Influence Diagrams Using Mixtures of Truncated Exponentials
    Download  web.ku.edu
    Mixtures of truncated exponentials (MTE) potentials are an alternative to discretization for representing continuous chance variables in influence diagrams. Also, MTE potentials c...
    Barry R. Cobb, Prakash P. Shenoy
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    UAI
    2004
    153views Artificial Intelligence» more  UAI 2004»
    A Logic Programming Framework for Possibilistic Argumentation with Vague Knowledge
    13 years 6 months ago
    A Logic Programming Framework for Possibilistic Argumentation with Vague Knowledge
    Download  cs.uns.edu.ar
    Defeasible argumentation frameworks have evolved to become a sound setting to formalize commonsense, qualitative reasoning from incomplete and potentially inconsistent knowledge. ...
    Carlos Iván Chesñevar, Guillermo Ric...
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    UAI
    2004
    100views Artificial Intelligence» more  UAI 2004»
    Sensitivity Analysis in Bayesian Networks: From Single to Multiple Parameters
    13 years 6 months ago
    Sensitivity Analysis in Bayesian Networks: From Single to Multiple Parameters
    Download  uai.sis.pitt.edu
    Previous work on sensitivity analysis in Bayesian networks has focused on single parameters, where the goal is to understand the sensitivity of queries to single parameter changes...
    Hei Chan, Adnan Darwiche
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    UAI
    2004
    114views Artificial Intelligence» more  UAI 2004»
    Applying Discrete PCA in Data Analysis
    13 years 6 months ago
    Applying Discrete PCA in Data Analysis
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    Wray L. Buntine, Aleks Jakulin
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    UAI
    2004
    105views Artificial Intelligence» more  UAI 2004»
    Compact Value-Function Representations for Qualitative Preferences
    13 years 6 months ago
    Compact Value-Function Representations for Qualitative Preferences
    Download  uai.sis.pitt.edu
    We consider the challenge of preference elicitation in systems that help users discover the most desirable item(s) within a given database. Past work on preference elicitation foc...
    Ronen I. Brafman, Carmel Domshlak, Tanya Kogan
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    UAI
    2004
    117views Artificial Intelligence» more  UAI 2004»
    On Finding Minimal w-cutset
    13 years 6 months ago
    On Finding Minimal w-cutset
    Download  uai.sis.pitt.edu
    The complexity of a reasoning task over a graphical model is tied to the induced width of the underlying graph. It is well-known that the conditioning (assigning values) on a subs...
    Bozhena Bidyuk, Rina Dechter
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    UAI
    2004
    95views Artificial Intelligence» more  UAI 2004»
    Computing Nash Equilibria of Action-Graph Games
    13 years 6 months ago
    Computing Nash Equilibria of Action-Graph Games
    Download  www.cs.ubc.ca
    Action-graph games (AGGs) are a fully expressive game representation which can compactly express both strict and context-specific independence between players' utility functi...
    Navin A. R. Bhat, Kevin Leyton-Brown
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    UAI
    2004
    141views Artificial Intelligence» more  UAI 2004»
    Recovering Articulated Object Models from 3D Range Data
    13 years 6 months ago
    Recovering Articulated Object Models from 3D Range Data
    Download  ai.stanford.edu
    We address the problem of unsupervised learning of complex articulated object models from 3D range data. We describe an algorithm whose input is a set of meshes corresponding to d...
    Dragomir Anguelov, Daphne Koller, Hoi-Cheung Pang,...
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    UAI
    2004
    114views Artificial Intelligence» more  UAI 2004»
    Using Arguments for Making Decisions: A Possibilistic Logic Approach
    13 years 6 months ago
    Using Arguments for Making Decisions: A Possibilistic Logic Approach
    Download  uai.sis.pitt.edu
    Humans currently use arguments for explaining choices which are already made, or for evaluating potential choices. Each potential choice has usually pros and cons of various stren...
    Leila Amgoud, Henri Prade
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    UAI
    2004
    110views Artificial Intelligence» more  UAI 2004»
    Maximum Entropy for Collaborative Filtering
    13 years 6 months ago
    Maximum Entropy for Collaborative Filtering
    Download  research.microsoft.com
    Within the task of collaborative filtering two challenges for computing conditional probabilities exist. First, the amount of training data available is typically sparse with resp...
    C. Lawrence Zitnick, Takeo Kanade
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