A longstanding goal in planning research is the ability to generalize plans developed for some set of environments to a new but similar environment, with minimal or no replanning....
Carlos Guestrin, Daphne Koller, Chris Gearhart, Ne...
A mobile robot acting in the world is faced with a large amount of sensory data and uncertainty in its action outcomes. Indeed, almost all interesting sequential decision-making d...
We study an approach to policy selection for large relational Markov Decision Processes (MDPs). We consider a variant of approximate policy iteration (API) that replaces the usual...
Relational Markov Decision Processes (MDP) are a useraction for stochastic planning problems since one can develop abstract solutions for them that are independent of domain size ...
Most traditional approaches to probabilistic planning in relationally specified MDPs rely on grounding the problem w.r.t. specific domain instantiations, thereby incurring a com...