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Mika Barkan and Gal A. Kaminka. Robots Predictive Execution Monitoring in BDI Recipes. In Proceedings of the 2019 AAMAS Workshop on Autonomous Robots and Multirobot Systems (ARMS), 2019.
Execution monitoring allows robots to assess the execution of plans, determine the need for re-planning, identify opportunities, and re-evaluate their commitments. There exists extensive literature on monitoring the execution of classical and HTN plans. However, execution monitoring of BDI plans is often left implicit in the BDI control loop. In practice, many BDI plan execution systems monitor the current plan steps only. They do not project ahead the current knowledge of the robot to determine implications on future steps. Thus a failure of a future plan-step, which may already be predictable given the current knowledge of the robot, is not detected until the last possible moment. This paper examines the task of predictive execution monitoring in BDI plans. It provides a base algorithm, and shows that its complexity is super-exponential in the general case, even under mild assumptions. It then discusses several methods for pruning the search space, and formally shows their completeness. It evaluates these methods in hundreds of experiments, utilizing approximately 4000 hours of modern CPU time.
@InProceedings{arms19ws,
author = {Mika Barkan and Gal A. Kaminka},
title = {Robots Predictive Execution Monitoring in BDI Recipes},
year = {2019},
booktitle = {Proceedings of the 2019 {AAMAS} Workshop on Autonomous Robots and Multirobot Systems ({ARMS})},
abstract = {Execution monitoring allows robots to assess the execution
of plans, determine the need for re-planning, identify opportunities, and
re-evaluate their commitments. There exists extensive literature on monitoring
the execution of classical and HTN plans. However, execution
monitoring of BDI plans is often left implicit in the BDI control loop. In
practice, many BDI plan execution systems monitor the current plan
steps only. They do not project ahead the current knowledge of the
robot to determine implications on future steps. Thus a failure of a
future plan-step, which may already be predictable given the current
knowledge of the robot, is not detected until the last possible moment.
This paper examines the task of predictive execution monitoring in BDI
plans. It provides a base algorithm, and shows that its complexity is
super-exponential in the general case, even under mild assumptions. It
then discusses several methods for pruning the search space, and formally
shows their completeness. It evaluates these methods in hundreds
of experiments, utilizing approximately 4000 hours of modern CPU time.
},
}
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