831 citations · 3.2k across the 65 of their papers we have counts for
6 papers · 2 filters
Decayed MCMC Filtering
Bhaskara Marthi, Hanna Pasula, Stuart Russell +1
Filtering---estimating the state of a partially observable Markov process from a sequence of observations---is one of the most widely studied problems in control theory, AI, and co…
Selecting Computations: Theory and Applications
Nicholas Hay, Stuart Russell, David Tolpin +1
Sequential decision problems are often approximately solvable by simulating possible future action sequences. {\em Metalevel} decision procedures have been developed for selecting…
General-Purpose MCMC Inference over Relational Structures
Brian Milch, Stuart Russell
Tasks such as record linkage and multi-target tracking, which involve reconstructing the set of objects that underlie some observed data, are particularly challenging for probabili…
Improving Gradient Estimation by Incorporating Sensor Data
Gregory Lawrence, Stuart Russell
An efficient policy search algorithm should estimate the local gradient of the objective function, with respect to the policy parameters, from as few trials as possible. Whereas mo…
RAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains
Emma Brunskill, Stuart Russell
Despite the intractability of generic optimal partially observable Markov decision process planning, there exist important problems that have highly structured models. Previous res…
A temporally abstracted Viterbi algorithm
Shaunak Chatterjee, Stuart Russell
Hierarchical problem abstraction, when applicable, may offer exponential reductions in computational complexity. Previous work on coarse-to-fine dynamic programming (CFDP) has demo…