Learning Graphical Models
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning. (Wikipedia)
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Reinforcement Learningwith State Observation Costsin Action-Contingent Noiselessly Observable Markov Decision Processes
Neural Information Processing Systems
Letb0 be twopositiveObservwill of V(b0) V (b0) after =O(ln (SAH /)), with 1 .
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Multi-agentactiveperceptionwithpredictionrewards
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Active perception,collecting observations to reduce uncertainty about ahidden variable, isone of the fundamental capabilities of an intelligent agent [2]. In multi-agent active perceptiona team of autonomous agents cooperatively gathers observations to infer the value of a hidden variable.
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