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ATAL
2010
Springer
15 years 7 months ago
Closing the learning-planning loop with predictive state representations
A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must learn an accurate model of ...
Byron Boots, Sajid M. Siddiqi, Geoffrey J. Gordon
MSWIM
2004
ACM
16 years 6 days ago
Scalable simulation of large-scale wireless networks with bounded inaccuracies
Discrete event network simulators have emerged as popular tools for verification and performance evaluation for various wireless networks. Nevertheless, the desire to model such ...
Zhengrong Ji, Junlan Zhou, Mineo Takai, Rajive Bag...
AAAI
2006
15 years 8 months ago
Decision Making in Uncertain Real-World Domains Using DT-Golog
DTGolog, a decision-theoretic agent programming language based on the situation calculus, was proposed to ease some of the computational difficulties associated with Markov Decisi...
Mikhail Soutchanski, Huy Pham, John Mylopoulos
CVPR
2009
IEEE
17 years 1 months ago
Efficient Kernels for Identifying Unbounded-Order Spatial Features
Higher order spatial features, such as doublets or triplets have been used to incorporate spatial information into the bag-of-local-features model. Due to computational limits, ...
Yimeng Zhang (Carnegie Mellon University), Tsuhan ...
CVPR
2004
IEEE
16 years 8 months ago
A Discriminative Learning Framework with Pairwise Constraints for Video Object Classification
In video object classification, insufficient labeled data may at times be easily augmented with pairwise constraints on sample points, i.e, whether they are in the same class or n...
Rong Yan, Jian Zhang, Jie Yang, Alexander G. Haupt...