A Logic-Based Approach to Finding Explanations for Discrepancies in Optimistic Plan Execution. Eiter, T., Erdem, E., Faber, W., & Senko, J. Fundamenta Informaticae, 79(1-2):25-69, 2008. Link abstract bibtex Consider an agent executing a plan with nondeterministic actions, in a dynamic environment, which might fail. Suppose that she is given a description of this action domain, including specifications of effects of actions, and a set of trajectories for the execution of this plan, where each trajectory specifies a possible execution of the plan in this domain. After executing some part of the plan, suppose that she obtains information about the current state of the world, and notices that she is not at a correct state relative to the given trajectories. How can she find an explanation (a point of failure) for such a discrepancy? An answer to this question can be useful for different purposes. In the context of execution monitoring, points of failure can determine some checkpoints that specify when to check for discrepancies, and they can sometimes be used for recovering from discrepancies that cause plan failures. At the modeling level, points of failure may provide useful insight into the action domain for a better understanding of the domain, or reveal errors in the formalization of the domain. We study the question above in a general logic-based knowledge representation framework, which can accommodate nondeterminism and concurrency. In this framework, we define a discrepancy and an explanation for it, and analyze the computational complexity of detecting discrepancies and finding explanations for them. We introduce a method for computing explanations, and report about a realization of this method using DLVK, which is a logic-programming based system for reasoning about actions and change.
@article{DBLP:journals/fuin/EiterEFS08,
author = {Thomas Eiter and
Esra Erdem and
Wolfgang Faber and
J{\'a}n Senko},
title = {A Logic-Based Approach to Finding Explanations for Discrepancies
in Optimistic Plan Execution},
journal = {Fundamenta Informaticae},
volume = {79},
number = {1-2},
year = {2008},
pages = {25-69},
ee = {http://iospress.metapress.com/openurl.asp?genre=article{\&}issn=0169-2968{\&}volume=79{\&}issue=1{\&}spage=25},
bibsource = {DBLP, http://dblp.uni-trier.de},
abstract = {Consider an agent executing a plan with nondeterministic actions,
in a dynamic environment, which might fail. Suppose that she is
given a description of this action domain, including specifications
of effects of actions, and a set of trajectories for the execution
of this plan, where each trajectory specifies a possible execution
of the plan in this domain. After executing some part of the plan,
suppose that she obtains information about the current state of
the world, and notices that she is not at a correct state relative
to the given trajectories. How can she find an explanation (a point
of failure) for such a discrepancy? An answer to this question can be
useful for different purposes. In the context of execution monitoring,
points of failure can determine some checkpoints that specify when
to check for discrepancies, and they can sometimes be used for
recovering from discrepancies that cause plan failures. At the modeling
level, points of failure may provide useful insight into the action
domain for a better understanding of the domain, or reveal errors
in the formalization of the domain. We study the question above in
a general logic-based knowledge representation framework, which can
accommodate nondeterminism and concurrency. In this framework,
we define a discrepancy and an explanation for it, and analyze
the computational complexity of detecting discrepancies and
finding explanations for them. We introduce a method for computing
explanations, and report about a realization of this method using
DLVK, which is a logic-programming based system for reasoning about
actions and change.
},
}
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Suppose that she is given a description of this action domain, including specifications of effects of actions, and a set of trajectories for the execution of this plan, where each trajectory specifies a possible execution of the plan in this domain. After executing some part of the plan, suppose that she obtains information about the current state of the world, and notices that she is not at a correct state relative to the given trajectories. How can she find an explanation (a point of failure) for such a discrepancy? An answer to this question can be useful for different purposes. In the context of execution monitoring, points of failure can determine some checkpoints that specify when to check for discrepancies, and they can sometimes be used for recovering from discrepancies that cause plan failures. At the modeling level, points of failure may provide useful insight into the action domain for a better understanding of the domain, or reveal errors in the formalization of the domain. We study the question above in a general logic-based knowledge representation framework, which can accommodate nondeterminism and concurrency. In this framework, we define a discrepancy and an explanation for it, and analyze the computational complexity of detecting discrepancies and finding explanations for them. We introduce a method for computing explanations, and report about a realization of this method using DLVK, which is a logic-programming based system for reasoning about actions and change. 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After executing some part of the plan,\nsuppose that she obtains information about the current state of\nthe world, and notices that she is not at a correct state relative\nto the given trajectories. How can she find an explanation (a point\nof failure) for such a discrepancy? An answer to this question can be\nuseful for different purposes. In the context of execution monitoring,\npoints of failure can determine some checkpoints that specify when\nto check for discrepancies, and they can sometimes be used for\nrecovering from discrepancies that cause plan failures. At the modeling\nlevel, points of failure may provide useful insight into the action\ndomain for a better understanding of the domain, or reveal errors\nin the formalization of the domain. We study the question above in\na general logic-based knowledge representation framework, which can\naccommodate nondeterminism and concurrency. 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