Robust Planning as Probabilistic Inference
Creating robust plans for the Minecraft planner of the PDDL Gym library using Probablistisitic Inference
M.S. Bonke (TU Delft - Electrical Engineering, Mathematics and Computer Science)
I.K. Hanou – Mentor (TU Delft - Algorithmics)
R.J. Gardos Reid – Mentor (TU Delft - Algorithmics)
S. Dumancic – Mentor (TU Delft - Algorithmics)
N.M. Gürel – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
All over the world, people plan their daily activities. These plans include a lot of different tasks and can vary widely in kinds of activities. These plans must account for uncertainties and unknowns in the world. Planning around these uncertainties is difficult and hard to accomplish with traditional means of programming. For this set of problems, probabilistic programming is proposed. Given the Minecraft planner from the Planning Domain Definition Language (PDDL) gym library, is it possible to create Robust plans that incorporate inference without changing the underlying planner? "PDDL is a human-readable format for problems in automated planning that gives a description of the possible states of the world, a description of the set of possible actions, a specific initial state of the world, and a specific set of desired goals." [6] The current approach is using heuristics to find the optimal plan for the problem. In this research paper, an alternative method is proposed; using probabilistic programming and the existing planner to create a simulated world of Minecraft. This model introduces inference without changing the already existing planner.