We investigate the problem of learning action effects in partially observable STRIPS planning domains. Our approach is based on a voted kernel perceptron learning model, where act...
Planners from the family of Graphplan (Graphplan, IPP, STAN...) are presently considered as the most efficient ones on numerous planning domains. Their partially ordered plans can...
The 3rd and 4th International Planning Competitions have enriched the set of benchmarks for classical propositional planning by a number of novel and interesting planning domains....
We describe and evaluate a system for learning domainspecific control knowledge. In particular, given a planning domain, the goal is to output a control policy that performs well ...
— For a robot to be able to first understand and then achieve a human’s goals, it must be able to reason about a) the context of the current situation (with respect to which i...