TY - GEN

T1 - Safe Learning of Lifted Action Models

AU - Juba, Brendan

AU - Le, Hai S.

AU - Stern, Roni

N1 - Funding Information:
This research is partially funded by NSF awards IIS-1908287, IIS-1939677, and CCF-1718380, and BSF grant #2018684 to Roni Stern.
Publisher Copyright:
Copyright © 2021, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

PY - 2021/1/1

Y1 - 2021/1/1

N2 - Creating a domain model, even for classical, domain-independent planning, is a notoriously hard knowledge-engineering task. A natural approach to solve this problem is to learn a domain model from observations. However, model learning approaches frequently do not provide safety guarantees: the learned model may assume actions are applicable when they are not, and may incorrectly capture actions' effects. This may result in generating plans that will fail when executed. In some domains such failures are not acceptable, due to the cost of failure or inability to replan online after failure. In such settings, all learning must be done offline, based on some observations collected, e.g., by some other agents or a human. Through this learning, the task is to generate a plan that is guaranteed to be successful. This is called the model-free planning problem. Prior work proposed an algorithm for solving the model-free planning problem in classical planning. However, they were limited to learning grounded domains, and thus they could not scale. We generalize this prior work and propose the first safe model-free planning algorithm for lifted domains. We prove the correctness of our approach, and provide a statistical analysis showing that the number of trajectories needed to solve future problems with high probability is linear in the potential size of the domain model. We also present experiments on twelve IPC domains showing that our approach is able to learn the real action model in all cases with at most two trajectories.

AB - Creating a domain model, even for classical, domain-independent planning, is a notoriously hard knowledge-engineering task. A natural approach to solve this problem is to learn a domain model from observations. However, model learning approaches frequently do not provide safety guarantees: the learned model may assume actions are applicable when they are not, and may incorrectly capture actions' effects. This may result in generating plans that will fail when executed. In some domains such failures are not acceptable, due to the cost of failure or inability to replan online after failure. In such settings, all learning must be done offline, based on some observations collected, e.g., by some other agents or a human. Through this learning, the task is to generate a plan that is guaranteed to be successful. This is called the model-free planning problem. Prior work proposed an algorithm for solving the model-free planning problem in classical planning. However, they were limited to learning grounded domains, and thus they could not scale. We generalize this prior work and propose the first safe model-free planning algorithm for lifted domains. We prove the correctness of our approach, and provide a statistical analysis showing that the number of trajectories needed to solve future problems with high probability is linear in the potential size of the domain model. We also present experiments on twelve IPC domains showing that our approach is able to learn the real action model in all cases with at most two trajectories.

UR - http://www.scopus.com/inward/record.url?scp=85125160083&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85125160083

T3 - Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning, KR 2021

SP - 379

EP - 389

BT - Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning, KR 2021

A2 - Bienvenu, Meghyn

A2 - Lakemeyer, Gerhard

A2 - Erdem, Esra

PB - International Joint Conferences on Artificial Intelligence Organization (IJCAI Organization)

T2 - 18th International Conference on Principles of Knowledge Representation and Reasoning, KR 2021

Y2 - 3 November 2021 through 12 November 2021

ER -