2010
DOI: 10.1017/s0269888910000160
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Validation and verification issues in a timeline-based planning system

Abstract: To foster effective use of artificial intelligence planning and scheduling (P&S) systems in the real world, it is of great importance to both (a) broaden direct access to the technology for the end users and (b) significantly increase their trust in such technology. Automated P&S systems often bring solutions to the users that are neither ‘obvious’ nor immediately acceptable to them. This is because these tools directly reason on causal, temporal, and resource constraints; moreover, they employ resolution proc… Show more

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Cited by 20 publications
(22 citation statements)
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References 20 publications
(50 reference statements)
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“…Both the model and the property are provided as inputs to model checking tool as shown in Figure 6.26. The translation from the MrSPOCK planning model to a model checker formal model requires introduction of a well-defined set of state variables and clocks [88]. State variables range on domain states, while clocks are used to represent time progression.…”
Section: Validation and Verificationmentioning
confidence: 99%
“…Both the model and the property are provided as inputs to model checking tool as shown in Figure 6.26. The translation from the MrSPOCK planning model to a model checker formal model requires introduction of a well-defined set of state variables and clocks [88]. State variables range on domain states, while clocks are used to represent time progression.…”
Section: Validation and Verificationmentioning
confidence: 99%
“…A problem for a wider diffusion of such technology stems in the limited community that has been studying with formal methodologies the properties of this planning approach. These authors have been investigating the interconnection between timeline-based planning and standard techniques for formal validation and verification (V&V) with the aim of building a rich environment for knowledge engineering [6] and exploring properties that concern temporal plans and their execution [7].…”
Section: Introductionmentioning
confidence: 99%
“…stakeholders, experts, users) still emerge after plan generation and execution. For example, the identification of unsatisfactory solutions (valid plans but undesirable from the users' perspective) and unbalanced trade-offs among different quality metrics and criteria (JÓNSSON, 2009;ENGELHARDT;CHIEN, 2000;CESTA et al, 2008) indicates a lack of understanding of the requirements and preferences in the model. These hidden requirements raise the need for iterative re-modeling and tuning process.…”
Section: Plan Analysis and Post-designmentioning
confidence: 99%
“…In some applications (such as space exploration (CESTA et al, 2008)) there are a number of criteria to evaluate a plan and complex trade-offs to be balanced. As discussed in Chapter 2, the definition of quality metric must be made wisely and carefully in AI planning problems.…”
Section: Long-term Plan Analysismentioning
confidence: 99%
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