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Stochastic Planning with Lifted Symbolic Trajectory Optimization

Cui, Hao and Keller, Thomas and Khardon, Roni. (2019) Stochastic Planning with Lifted Symbolic Trajectory Optimization. In: Proceedings of the 29th International Conference on Automated Planning and Scheduling. pp. 119-127.

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Official URL: https://edoc.unibas.ch/74993/

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Abstract

This paper investigates online stochastic planning for problems with large factored state and action spaces. One promising approach in recent work estimates the quality of applicable actions in the current state through aggregate simulation from the states they reach. This leads to significant speedup, compared to search over concrete states and actions, and suffices to guide decision making in cases where the performance of a random policy is informative of the quality of a state. The paper makes two significant improvements to this approach. The first, taking inspiration from lifted belief propagation, exploits the structure of the problem to derive a more compact computation graph for aggregate simulation. The second improvement replaces the random policy embedded in the computation graph with symbolic variables that are optimized simultaneously with the search for high quality actions. This expands the scope of the approach to problems that require deep search and where information is lost quickly with random steps. An empirical evaluation shows that these ideas significantly improve performance, leading to state of the art performance on hard planning problems.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Artificial Intelligence (Helmert)
UniBasel Contributors:Keller, Thomas
Item Type:Conference or Workshop Item, refereed
Conference or workshop item Subtype:Conference Paper
Publisher:AAAI Press
e-ISSN:2334-0843
Note:Publication type according to Uni Basel Research Database: Conference paper
Last Modified:10 Mar 2020 12:45
Deposited On:10 Mar 2020 12:44

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