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20 papers · 1 filter
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…
Algorithms for Irrelevance-Based Partial MAPs
Solomon Eyal Shimony
Irrelevance-based partial MAPs are useful constructs for domain-independent explanation using belief networks. We look at two definitions for such partial MAPs, and prove important…
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…
Distributed Random Walks
Atish Das Sarma, Danupon Nanongkai, Gopal Pandurangan +1
Performing random walks in networks is a fundamental primitive that has found applications in many areas of computer science, including distributed computing. In this paper, we foc…