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    AAAI
    1998
    152views Intelligent Agents» more  AAAI 1998»
    Boosting in the Limit: Maximizing the Margin of Learned Ensembles
    13 years 6 months ago
    Boosting in the Limit: Maximizing the Margin of Learned Ensembles
    Download  reference.kfupm.edu.sa
    The "minimum margin" of an ensemble classifier on a given training set is, roughly speaking, the smallest vote it gives to any correct training label. Recent work has sh...
    Adam J. Grove, Dale Schuurmans
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    AAAI
    1998
    150views Intelligent Agents» more  AAAI 1998»
    Bayesian Network Models for Generation of Crisis Management Training Scenarios
    13 years 6 months ago
    Bayesian Network Models for Generation of Crisis Management Training Scenarios
    Download  www.aaai.org
    We present a noisy-OR Bayesian network model for simulation-based training, and an efficient search-based algorithm for automatic synthesis of plausible training scenarios from co...
    Eugene Grois, William H. Hsu, Mikhail Voloshin, Da...
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    AAAI
    1998
    160views Intelligent Agents» more  AAAI 1998»
    Boosting Combinatorial Search Through Randomization
    13 years 6 months ago
    Boosting Combinatorial Search Through Randomization
    Download  www.cs.ubc.ca
    Unpredictability in the running time of complete search procedures can often be explained by the phenomenon of "heavy-tailed cost distributions", meaning that at any tim...
    Carla P. Gomes, Bart Selman, Henry A. Kautz
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    AAAI
    1998
    102views Intelligent Agents» more  AAAI 1998»
    Act, and the Rest Will Follow: Exploiting Determinism in Planning as Satisfiability
    13 years 6 months ago
    Act, and the Rest Will Follow: Exploiting Determinism in Planning as Satisfiability
    Download  www.aaai.org
    Enrico Giunchiglia, Alessandro Massarotto, Roberto...
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    AAAI
    1998
    132views Intelligent Agents» more  AAAI 1998»
    Supermodels and Robustness
    13 years 6 months ago
    Supermodels and Robustness
    Download  www.cirl.uoregon.edu
    When search techniques are used to solve a practical problem, the solution produced is often brittle in the sense that small execution difficulties can have an arbitrarily large e...
    Matthew L. Ginsberg, Andrew J. Parkes, Amitabha Ro...
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    AAAI
    1998
    112views Intelligent Agents» more  AAAI 1998»
    Learning to Predict User Operations for Adaptive Scheduling
    13 years 6 months ago
    Learning to Predict User Operations for Adaptive Scheduling
    Download  www.aaai.org
    Mixed-initiativesystemspresent the challengeof finding an effective level of interaction betweenhumans and computers. Machinelearning presents a promising approachto this problemi...
    Melinda T. Gervasio, Wayne Iba, Pat Langley
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    AAAI
    1998
    164views Intelligent Agents» more  AAAI 1998»
    Procedural Help in Andes: Generating Hints Using a Bayesian Network Student Model
    13 years 6 months ago
    Procedural Help in Andes: Generating Hints Using a Bayesian Network Student Model
    Download  www.cs.ubc.ca
    One of the most important problems for an intelligent tutoring system is deciding how to respond when a student asks for help. Responding cooperatively requires an understanding o...
    Abigail S. Gertner, Cristina Conati, Kurt VanLehn
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    AAAI
    1998
    90views Intelligent Agents» more  AAAI 1998»
    Inferring State Constraints for Domain-Independent Planning
    13 years 6 months ago
    Inferring State Constraints for Domain-Independent Planning
    Download  www.aaai.org
    Wedescribe somenewpreprocessing techniques that enable faster domain-independentplanning. Thefirst set of techniquesis aimedat inferring state constraints from the structure of pl...
    Alfonso Gerevini, Lenhart K. Schubert
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    AAAI
    1998
    127views Intelligent Agents» more  AAAI 1998»
    A Feature-Based Learning Method for Theorem Proving
    13 years 6 months ago
    A Feature-Based Learning Method for Theorem Proving
    Download  www.aaai.org
    Automatedreasoning or theorem proving essentially amounts to solving search problems. Despite significant progress in recent years theorem provers still have manyshortcomings. The...
    Matthias Fuchs
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    AAAI
    1998
    146views Intelligent Agents» more  AAAI 1998»
    Structured Representation of Complex Stochastic Systems
    13 years 6 months ago
    Structured Representation of Complex Stochastic Systems
    Download  ai.stanford.edu
    This paperconsidersthe problem of representingcomplex systems that evolve stochastically over time. Dynamic Bayesian networks provide a compact representation for stochastic proce...
    Nir Friedman, Daphne Koller, Avi Pfeffer
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