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EMO
2009
Springer
174views Optimization» more  EMO 2009»
14 years 3 months ago
Constraint Programming
To model combinatorial decision problems involving uncertainty and probability, we introduce stochastic constraint programming. Stochastic constraint programs contain both decision...
Pascal Van Hentenryck
LICS
1991
IEEE
14 years 13 days ago
Actions Speak Louder than Words: Proving Bisimilarity for Context-Free Processes
Baeten, Bergstra, and Klop (and later Caucal) have proved the remarkable result that bisimulation equivalence is decidable for irredundant context-free grammars. In this paper we ...
Hans Hüttel, Colin Stirling
ICML
1994
IEEE
14 years 13 days ago
Reducing Misclassification Costs
We explore algorithms for learning classification procedures that attempt to minimize the cost of misclassifying examples. First, we consider inductive learning of classification ...
Michael J. Pazzani, Christopher J. Merz, Patrick M...
NIPS
2008
13 years 10 months ago
Particle Filter-based Policy Gradient in POMDPs
Our setting is a Partially Observable Markov Decision Process with continuous state, observation and action spaces. Decisions are based on a Particle Filter for estimating the bel...
Pierre-Arnaud Coquelin, Romain Deguest, Rém...
AAAI
2004
13 years 10 months ago
Stochastic Local Search for POMDP Controllers
The search for finite-state controllers for partially observable Markov decision processes (POMDPs) is often based on approaches like gradient ascent, attractive because of their ...
Darius Braziunas, Craig Boutilier