Academic Journal
A Multi-Level Validation and Traceability Framework for AI-Generated Telescope Scheduling Decisions.
| Τίτλος: | A Multi-Level Validation and Traceability Framework for AI-Generated Telescope Scheduling Decisions. |
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| Συγγραφείς: | Xiao, Hengchu, Wang, Chuanjun |
| Πηγή: | Universe (2218-1997); Jun2026, Vol. 12 Issue 6, p172, 21p |
| Θεματικοί όροι: | Automated planning & scheduling, Model validation, Software frameworks, Reliability in engineering, Decision making, Constraint satisfaction |
| Περίληψη: | With the gradual introduction of AI into telescope scheduling, AI-based decision-making has shown advantages in handling complex multi-constraint problems. However, its outputs often suffer from inconsistent data references, reasoning errors, and non-executable decisions, limiting applicability in high-reliability observational tasks. In this work, we propose a multi-level validation and traceable reasoning framework that performs systematic reliability verification of AI-generated decisions prior to execution, and enables explicit representation of the reasoning process to support traceable decision-making. The framework integrates data reference validation, logical consistency checks, and observational and instrumental constraint verification to filter and correct invalid decisions. It also introduces atomic reasoning units and their dependency relationships, representing scheduling decisions as a sequence of interconnected reasoning steps that support error localization and post hoc analysis. Experiments show that the framework improves executability and reliability of AI scheduling and reduces loss of transient opportunities. In particular, feedback correction and structured validation of reasoning steps enhance the ability to repair and block erroneous decisions, especially in complex scenarios. Compared with pure AI methods, the framework-enhanced approach maintains flexibility while substantially improving reliability and executability. These results demonstrate a feasible and verifiable pathway for applying AI to high-reliability astronomical observation scheduling. [ABSTRACT FROM AUTHOR] |
| Copyright of Universe (2218-1997) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: A Multi-Level Validation and Traceability Framework for AI-Generated Telescope Scheduling Decisions. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xiao%2C+Hengchu%22">Xiao, Hengchu</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Chuanjun%22">Wang, Chuanjun</searchLink> – Name: TitleSource Label: Source Group: Src Data: Universe (2218-1997); Jun2026, Vol. 12 Issue 6, p172, 21p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Automated+planning+%26+scheduling%22">Automated planning & scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Model+validation%22">Model validation</searchLink><br /><searchLink fieldCode="DE" term="%22Software+frameworks%22">Software frameworks</searchLink><br /><searchLink fieldCode="DE" term="%22Reliability+in+engineering%22">Reliability in engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+making%22">Decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Constraint+satisfaction%22">Constraint satisfaction</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With the gradual introduction of AI into telescope scheduling, AI-based decision-making has shown advantages in handling complex multi-constraint problems. However, its outputs often suffer from inconsistent data references, reasoning errors, and non-executable decisions, limiting applicability in high-reliability observational tasks. In this work, we propose a multi-level validation and traceable reasoning framework that performs systematic reliability verification of AI-generated decisions prior to execution, and enables explicit representation of the reasoning process to support traceable decision-making. The framework integrates data reference validation, logical consistency checks, and observational and instrumental constraint verification to filter and correct invalid decisions. It also introduces atomic reasoning units and their dependency relationships, representing scheduling decisions as a sequence of interconnected reasoning steps that support error localization and post hoc analysis. Experiments show that the framework improves executability and reliability of AI scheduling and reduces loss of transient opportunities. In particular, feedback correction and structured validation of reasoning steps enhance the ability to repair and block erroneous decisions, especially in complex scenarios. Compared with pure AI methods, the framework-enhanced approach maintains flexibility while substantially improving reliability and executability. These results demonstrate a feasible and verifiable pathway for applying AI to high-reliability astronomical observation scheduling. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Universe (2218-1997) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/universe12060172 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 172 Subjects: – SubjectFull: Automated planning & scheduling Type: general – SubjectFull: Model validation Type: general – SubjectFull: Software frameworks Type: general – SubjectFull: Reliability in engineering Type: general – SubjectFull: Decision making Type: general – SubjectFull: Constraint satisfaction Type: general Titles: – TitleFull: A Multi-Level Validation and Traceability Framework for AI-Generated Telescope Scheduling Decisions. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xiao, Hengchu – PersonEntity: Name: NameFull: Wang, Chuanjun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 22181997 Numbering: – Type: volume Value: 12 – Type: issue Value: 6 Titles: – TitleFull: Universe (2218-1997) Type: main |
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