Douglas Aberdeen

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Partially observable Markov decision processes (POMDPs) are interesting because they provide a general framework for learning in the presence of multiple forms of uncertainty. We survey methods for learning within the POMDP framework. Because exact methods are intractable we concentrate on approximate methods. We explore two versions of the POMDP training(More)
Partially observable Markov decision processes are interesting because of their ability to model most conceivable real-world learning problems, for example, robot navigation, driving a car, speech recognition, stock trading, and playing games. The downside of this generality is that exact algorithms are computationally intractable. Such computational(More)
The Priority Inbox feature of Gmail ranks mail by the probability that the user will perform an action on that mail. Because “importance” is highly personal, we try to predict it by learning a per-user statistical model, updated as frequently as possible. This research note describes the challenges of online learning over millions of models, and the(More)
Artificial neural networks with millions of adjustable parameters and a similar number of training examples are a potential solution for difficult, large-scale pattern recognition problems in areas such as speech and face recognition, classification of large volumes of web data, and finance. The bottleneck is that neural network training involves iterative(More)
Stochastic Shortest Path problems (SSPs), a subclass of Markov Decision Problems (MDPs), can be efficiently dealt with using Real-Time Dynamic Programming (RTDP). Yet, MDP models are often uncertain (obtained through statistics or guessing). The usual approach is robust planning: searching for the best policy under the worst model. This paper shows how RTDP(More)