We detail some lessons learned while designing and testing a decision-theoretic advising
support tool for undergraduates at a large state university. Between 2009 and 2011 we …

Monte Carlo tree search (MCTS) algorithms can encounter difficulties when solving Markov
decision processes (MDPs) in which the outcomes of actions are highly stochastic. This …

We present a novel approach for decreasing state uncertainty in planning prior to solving the
planning problem. This is done by making predictions about the state based on currently …

We consider the problem of allocating applicants to courses, where each applicant has a
subset of acceptable courses that she ranks in strict order of preference. Each applicant and …

Real world robotics often operates in uncertain and dynamic environments where
generalisation over different scenarios is of practical interest. In the absence of a model …

Monte Carlo tree search (MCTS) is a class of online planning algorithms for Markov decision
processes (MDPs) and related models that has found success in challenging applications. In …

Abstract Factored Markov Decision Processes (MDP) are a de facto standard for compactly
modeling sequential decision making problems with uncertainty. Offline planning based on …

The international planning competition (IPC) is a recurring event that compares the
performance of planners and awards the best ones. The evaluation is based on a set of …

Abstract Markov Decision Processes (MDPs) are the de-facto formalism for studying
sequential decision making problems with uncertainty, ranging from classical problems such …

While recent advances in offline reasoning techniques and online execution strategies have
made planning under uncertainty more robust, the application of plans in partially-known …