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...
Probabilistic planning problems are typically modeled as a Markov Decision Process (MDP). MDPs, while an otherwise expressive model, allow only for sequential, non-durative action...
Symbolic non-deterministic planning represents action effects as sets of possible next states. In this paper, we move toward a more probabilistic uncertainty model by distinguishi...
Rune M. Jensen, Manuela M. Veloso, Randal E. Bryan...
MostAI representations and algorithms for plan generation havenot included the concept of informationproducingactions (also called diagnostics, or tests, in the decision making li...
Planning as heuristic search is a powerful approach to solving domain independent planning problems. In recent years, various successful heuristics and planners like FF, LPG, FAST...
Martin Wehrle, Sebastian Kupferschmid, Andreas Pod...