Thomas L. Dean

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Planning under uncertainty is a central problem in the study of automated sequential decision making and has been addressed by researchers in many di erent elds including AI planning decision analysis operations research control theory and economics While the assumptions and perspectives adopted in these areas often di er in substantial ways many planning(More)
This paper presents a framework for exploring issues in time-dependent planning: planning in which the time available to respond to predicted events varies, and the decision making required to formulate effective responses is complex. Our analysis of time-dependent planning suggests an approach based on a class of algorithms that we call anytime algorithms.(More)
Many stochastic planning problems can be represented using Markov Decision Processes (MDPs). A difficulty with using these MDP representations is that the common algorithms for solving them run in time polynomial in the size of the state space, where this size is extremely large for most real-world planning problems of interest. Recent AI research has(More)
A planning problem is time-dependent, if the time spent planning affects the uti l i ty of the system's performance. In [Dean and Boddy, 1988], we define a framework for constructing solutions to time-dependent planning problems, called expectation-driven iterative refinement. In this paper, we analyze and solve a moderately complex time-dependent planning(More)
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 macro-actions and leave the state space unchanged, we propose a hierarchical model (using an abstract MDP) that works with macro-actions only, and that significantly reduces the(More)
Each year, AAAI’s National Conference on Artificial Intelligence honors a handful of papers that exemplify high standards in exposition and pedagogy. Papers are nominated for the Best Written Paper Award by members of the program committee during the NCAI review process. These nominations are then reviewed once again by a smaller subset of the program(More)
We provide a method based on the theory of Markov decision processes for e cient planning in stochastic domains Goals are encoded as reward functions expressing the desirability of each world state the planner must nd a policy mapping from states to actions that maximizes future rewards Standard goals of achievement as well as goals of maintenance and(More)