234 citations · 375 across the 4 of their papers we have counts for
4 papers
Integrating Planning and Execution in Stochastic Domains
Richard Dearden, Craig Boutilier
We investigate planning in time-critical domains represented as Markov Decision Processes, showing that search based techniques can be a very powerful method for finding close to o…
Model-Based Bayesian Exploration
Richard Dearden, Nir Friedman, David Andre
Reinforcement learning systems are often concerned with balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of explorat…
Planning under Continuous Time and Resource Uncertainty: A Challenge for AI
John Bresina, Richard Dearden, Nicolas Meuleau +3
We outline a class of problems, typical of Mars rover operations, that are problematic for current methods of planning under uncertainty. The existing methods fail because they suf…
Dynamic Programming for Structured Continuous Markov Decision Problems
Zhengzhu Feng, Richard Dearden, Nicolas Meuleau +1
We describe an approach for exploiting structure in Markov Decision Processes with continuous state variables. At each step of the dynamic programming, the state space is dynamical…