Researchers have developed a huge number of algorithms to solve classical planning problems. We provide a way to use these algorithms, unmodified, to generate strong-cyclic soluti...
Ugur Kuter, Dana S. Nau, Elnatan Reisner, Robert P...
This article investigates fundamental issues in scaling autonomous personal robots towards open-ended sets of everyday manipulation tasks which involve high complexity and vague j...
Markov decision processes (MDPs) and contingency planning (CP) are two widely used approaches to planning under uncertainty. MDPs are attractive because the model is extremely gen...
In this paper we propose a suite of techniques for planning with temporally extended preferences (TEPs). To this end, we propose a method for compiling TEP planning problems into ...
Jorge A. Baier, Fahiem Bacchus, Sheila A. McIlrait...
This paper presents a novel idea, which combines Planning, Machine Learning and Knowledge-Based techniques. It is concerned with the development of an adaptive planning system tha...
Dimitris Vrakas, Grigorios Tsoumakas, Nick Bassili...