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AAAI
2006
13 years 9 months ago
Compact, Convex Upper Bound Iteration for Approximate POMDP Planning
Partially observable Markov decision processes (POMDPs) are an intuitive and general way to model sequential decision making problems under uncertainty. Unfortunately, even approx...
Tao Wang, Pascal Poupart, Michael H. Bowling, Dale...
AAAI
2000
13 years 9 months ago
Towards Feasible Approach to Plan Checking under Probabilistic Uncertainty: Interval Methods
The main problem of planning is to find a sequence of actions that an agent must perform to achieve a given objective. An important part of planning is checking whether a given pl...
Raul Trejo, Vladik Kreinovich, Chitta Baral
ICASSP
2011
IEEE
12 years 11 months ago
SRF: Matrix completion based on smoothed rank function
In this paper, we address the matrix completion problem and propose a novel algorithm based on a smoothed rank function (SRF) approximation. Among available algorithms like FPCA a...
Hooshang Ghasemi, Mohmmadreza Malek-Mohammadi, Mas...
IPSN
2004
Springer
14 years 29 days ago
A probabilistic approach to inference with limited information in sensor networks
We present a methodology for a sensor network to answer queries with limited and stochastic information using probabilistic techniques. This capability is useful in that it allows...
Rahul Biswas, Sebastian Thrun, Leonidas J. Guibas
FOCS
1995
IEEE
13 years 11 months ago
Free Bits, PCPs and Non-Approximability - Towards Tight Results
This paper continues the investigation of the connection between probabilistically checkable proofs PCPs the approximability of NP-optimization problems. The emphasis is on prov...
Mihir Bellare, Oded Goldreich, Madhu Sudan