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NIPS
2001
13 years 10 months ago
A Bayesian Model Predicts Human Parse Preference and Reading Times in Sentence Processing
Narayanan and Jurafsky (1998) proposed that human language comprehension can be modeled by treating human comprehenders as Bayesian reasoners, and modeling the comprehension proce...
S. Narayanan, Daniel Jurafsky
AAAI
1997
13 years 10 months ago
Incremental Methods for Computing Bounds in Partially Observable Markov Decision Processes
Partially observable Markov decision processes (POMDPs) allow one to model complex dynamic decision or control problems that include both action outcome uncertainty and imperfect ...
Milos Hauskrecht
ML
2002
ACM
143views Machine Learning» more  ML 2002»
13 years 8 months ago
A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes
An issue that is critical for the application of Markov decision processes MDPs to realistic problems is how the complexity of planning scales with the size of the MDP. In stochas...
Michael J. Kearns, Yishay Mansour, Andrew Y. Ng
IPMU
2010
Springer
14 years 1 months ago
A New Adaptive Consensus Reaching Process Based on the Experts' Importance
Usually, in a group decision context, the importance level, confidence degree and amount of knowledge are very different among individuals. So, when all the individuals have to r...
Ignacio J. Pérez, Francisco Javier Cabreriz...
HICSS
2007
IEEE
111views Biometrics» more  HICSS 2007»
14 years 3 months ago
A Process-Centric Approach for Coordinating Product Configuration Decisions
In product family approaches such as software product lines, product configuration is a key activity that deals with the constrained combination and parameterization of reusable c...
Marcílio Mendonça, Donald D. Cowan, ...