Monitoring the Generation and Execution of Optimal Plans

Monitoring the Generation and Execution of Optimal Plans. Fritz, C. Ph.D. Thesis, University of Toronto, April, 2009. Best Thesis Runner-Up Award at ICAPS 2010.

Paper

Monitoring the Generation and Execution of Optimal Plans [link]

Web abstract bibtex 7 downloads

In dynamic domains, the state of the world may change in unexpected ways during the generation or execution of plans. Regardless of the cause of such changes, they raise the question of whether they interfere with ongoing planning efforts. Unexpected changes during plan generation may invalidate the current planning effort, while discrepancies between expected and actual state of the world during execution may render the executing plan invalid or sub-optimal, with respect to previously identified planning objectives.

In this thesis we develop a general monitoring technique that can be used during both plan generation and plan execution to determine the relevance of unexpected changes and which supports recovery. This way, time intensive replanning from scratch in the new and unexpected state can often be avoided. The technique can be applied to a variety of objectives, including monitoring the optimality of plans, rather then just their validity. Intuitively, the technique operates in two steps: during planning the plan is annotated with additional information that is relevant to the achievement of the objective; then, when an unexpected change occurs, this information is used to determine the relevance of the discrepancy with respect to the objective.

We substantiate the claim of broad applicability of this relevance-based technique by developing four concrete applications: generating optimal plans despite frequent, unexpected changes to the initial state of the world, monitoring plan optimality during execution, monitoring the execution of near-optimal policies in stochastic domains, and monitoring the generation and execution of plans with procedural hard constraints. In all cases, we use the formal notion of regression to identify what is relevant for achieving the objective. e prove the soundness of these concrete approaches and present empirical results demonstrating that in some contexts orders of magnitude speed-ups can be gained by our technique compared to replanning from scratch.

@PhdThesis{ fri-phd09,
  author = 	 {Christian Fritz},
  title = 	 {Monitoring the Generation and Execution of Optimal Plans},
  school = 	 {University of Toronto},
  year = 	 2009,
  month =	 {April},
  urlPaper= {Fritz_Christian_W_200906_PhD_thesis.pdf},
  urlWeb = {http://hdl.handle.net/1807/17763},
  abstract = {
<p>
In dynamic domains, the state of the world may change in
unexpected ways during the generation or execution of plans.
Regardless of the cause of such changes, they raise the question
of whether they interfere with ongoing planning efforts.
Unexpected changes during plan generation may invalidate the
current planning effort, while discrepancies between expected and
actual state of the world during execution may render the
executing plan invalid or sub-optimal, with respect to previously
identified planning objectives.
</p><p>
In this thesis we develop a general monitoring technique that can
be used during both plan generation and plan execution to
determine the relevance of unexpected changes and which supports
recovery. This way, time intensive replanning from scratch in the
new and unexpected state can often be avoided.
The technique can be applied to a variety of objectives,
including monitoring the optimality of plans, rather then just
their validity.
Intuitively, the technique operates in two steps: during planning
the plan is annotated with additional information that is
relevant to the achievement of the objective; then, when an
unexpected change occurs, this information is used to determine
the relevance of the discrepancy with respect to the objective.
</p><p>
We substantiate the claim of broad applicability of this
relevance-based technique by developing four concrete
applications:
generating optimal plans despite frequent, unexpected changes to
the initial state of the world, monitoring plan optimality during
execution, monitoring the execution of near-optimal policies in
stochastic domains, and monitoring the generation and execution
of plans with procedural hard constraints.
In all cases, we use the formal notion of regression to identify
what is relevant for achieving the objective. 
e prove the soundness of these concrete approaches and present
empirical results demonstrating that in some contexts orders of
magnitude speed-ups can be gained by our technique compared to
replanning from scratch.
</p>
},
  bibbase_note = {<span style="color: green">Best Thesis Runner-Up Award at ICAPS 2010.</span>},
  keywords = {Execution Monitoring}
}

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Unexpected changes during plan generation may invalidate the current planning effort, while discrepancies between expected and actual state of the world during execution may render the executing plan invalid or sub-optimal, with respect to previously identified planning objectives. In this thesis we develop a general monitoring technique that can be used during both plan generation and plan execution to determine the relevance of unexpected changes and which supports recovery. This way, time intensive replanning from scratch in the new and unexpected state can often be avoided. The technique can be applied to a variety of objectives, including monitoring the optimality of plans, rather then just their validity. Intuitively, the technique operates in two steps: during planning the plan is annotated with additional information that is relevant to the achievement of the objective; then, when an unexpected change occurs, this information is used to determine the relevance of the discrepancy with respect to the objective. We substantiate the claim of broad applicability of this relevance-based technique by developing four concrete applications: generating optimal plans despite frequent, unexpected changes to the initial state of the world, monitoring plan optimality during execution, monitoring the execution of near-optimal policies in stochastic domains, and monitoring the generation and execution of plans with procedural hard constraints. 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