1.1k citations · 1.8k across the 8 of their papers we have counts for
8 papers
Probabilistic Causal Reasoning
Thomas L. Dean, Keiji Kanazawa
Predicting the future is an important component of decision making. In most situations, however, there is not enough information to make accurate predictions. In this paper, we dev…
Map Learning with Indistinguishable Locations
Kenneth Basye, Thomas L. Dean
Nearly all spatial reasoning problems involve uncertainty of one sort or another. Uncertainty arises due to the inaccuracies of sensors used in measuring distances and angles. We r…
Deliberation Scheduling for Time-Critical Sequential Decision Making
Thomas L. Dean, Leslie Pack Kaelbling, Jak Kirman +1
We describe a method for time-critical decision making involving sequential tasks and stochastic processes. The method employs several iterative refinement routines for solving dif…
On the Complexity of Solving Markov Decision Problems
Michael L. Littman, Thomas L. Dean, Leslie Pack Kaelbling
Markov decision problems (MDPs) provide the foundations for a number of problems of interest to AI researchers studying automated planning and reinforcement learning. In this paper…
Model Reduction Techniques for Computing Approximately Optimal Solutions for Markov Decision Processes
Thomas L. Dean, Robert Givan, Sonia Leach
We present a method for solving implicit (factored) Markov decision processes (MDPs) with very large state spaces. We introduce a property of state space partitions which we call e…
Hierarchical Solution of Markov Decision Processes using Macro-actions
Milos Hauskrecht, Nicolas Meuleau, Leslie Pack Kaelbling +2
We investigate the use of temporally abstract actions, or macro-actions, in the solution of Markov decision processes. Unlike current models that combine both primitive actions and…