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Predictive Execution Monitoring for Layered Hierarchical Recipes

Mika Barkan. Predictive Execution Monitoring for Layered Hierarchical Recipes. Master's Thesis, Bar Ilan University,2020.

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Abstract

Execution monitoring allows agents to assess plan execution progress, determine the need for re-planning, identify opportunities, and re-evaluate their commitments. While there exists extensive literature on execution monitoring of classical and HTN plans, monitoring of layered hierarchical recipes is typically myopic, discovering failures late in the execution, even if a failure of a future step may already be determined given the current knowledge of the agent. This thesis examines the task of predictive execution monitoring in layered hierarchical recipes. 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 to determine what nodes where visited thus reducing the search space, and formally shows their completeness. Then we explore how using the results of previous calls to execution monitoring can help reduce the time to execute it again. It evaluates these methods in hundreds of experiments, and on a NAO robot.

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BibTeX

@mastersthesis{barkan-msc,
  author = {Mika Barkan},
  title = {Predictive Execution Monitoring for Layered Hierarchical Recipes},
  school = {{B}ar {I}lan {U}niversity},
  year = {2020},
  OPTkey = {},
  OPTtype = {},
  OPTaddress = {},
  OPTmonth = {},
  OPTnote = {Available at \url{http://www.cs.biu.ac.il/~galk/Publications/b2hd-barkan-msc.html}},
  OPTannote = {},
  wwwnote = {}, 
  abstract = {Execution monitoring allows agents to assess plan execution progress, determine the need for re-planning, identify opportunities, and re-evaluate their commitments.  While there exists extensive literature on execution monitoring of classical and HTN plans, monitoring of \emph{layered hierarchical recipes} is typically myopic,  discovering failures late in the execution, even if a failure of a future step may already be determined given the current knowledge of the agent.   This thesis examines the task of predictive execution monitoring in layered hierarchical recipes. 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 to determine what nodes where visited thus reducing the search space, and formally shows their completeness. Then we explore how using the results of previous calls to execution monitoring can help reduce the time to execute it again. It evaluates these methods in hundreds of experiments, and on a NAO robot. },
}

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