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Record W4382866772 · doi:10.1609/icaps.v33i1.27251

Frontmatter

2023· article· en· W4382866772 on OpenAlexfundno aff
Roman Barták, Andrea Orlandini, Mauro Vallati, Sven Koenig, Roni Stern

Bibliographic record

VenueProceedings of the International Conference on Automated Planning and Scheduling · 2023
Typearticle
Languageen
FieldComputer Science
TopicConstraint Satisfaction and Optimization
Canadian institutionsnot available
FundersMoonshot Research and Development ProgramOffice of Naval ResearchOffice National d'études et de Recherches AérospatialesInstitute of Software, Chinese Academy of SciencesU.S. Naval Research LaboratoryRussian Academy of SciencesTechnische Universität DortmundSingapore Management UniversityUniversität KonstanzLibera Università di BolzanoUniversità degli Studi di BresciaUniversidade do PortoUniversidad de GranadaMasarykova UniverzitaUniversität des SaarlandesRWTH Aachen UniversityDanmarks Tekniske UniversitetUniversitat Politècnica de ValènciaBen-Gurion University of the NegevUniversidade Federal do Rio Grande do SulTechnische Universität WienSapienza Università di RomaOregon State UniversityQueen's UniversityLinköpings UniversitetUniversity of AlbertaImperial College LondonUniversity of TorontoUniversity of Southern CaliforniaInstitució Catalana de Recerca i Estudis AvançatsArizona State UniversityUniversitat Pompeu FabraTechnische Universiteit EindhovenChinese Academy of SciencesSun Yat-sen UniversityBar-Ilan UniversityUniversiteit van AmsterdamJet Propulsion LaboratoryMonash UniversityRoyal Holloway, University of LondonNational Aeronautics and Space AdministrationNanyang Technological UniversityUniversität UlmNational University of SingaporeUniversity of AberdeenUniversity of OxfordAlbert-Ludwigs-Universität FreiburgCentre National de la Recherche ScientifiqueKing's College LondonInvitaeCarnegie Mellon UniversityAmazon RoboticsPurdue UniversityPolytechnique MontréalInstitut national de recherche en informatique et en automatique (INRIA)Universite AngersColorado State UniversityKU LeuvenHeriot-Watt UniversitySimon Fraser UniversityUniversità degli Studi di PadovaEuropean Space AgencyAalborg UniversitetUniversidad Carlos III de MadridInternational Science and Technology CenterUniversità degli Studi di Napoli Federico IIÖrebro UniversitetCalifornia Institute of TechnologyFondazione Bruno KesslerGeorgia Institute of TechnologyUniversidad de OviedoUniversität BaselUniversità degli Studi di TrentoUniversity of Science and Technology of ChinaMicrosoft ResearchTechnische Universiteit DelftRAND CorporationČeské Vysoké Učení Technické v Praze
KeywordsCzechComputer scienceLibrary scienceScheduling (production processes)Operations researchPolitical scienceEngineeringOperations management

Abstract

fetched live from OpenAlex

This volume contains the papers accepted for presentation at ICAPS 2023, the Thirty-Third International Conference on Automated Planning and Scheduling, to be held in Prague, Czech Republic, July 8-13, 2023. The annual ICAPS conference series was formed in 2003 through the merger of two pre-existing biennial conferences, the International Conference on Artificial Intelligence Planning and Scheduling (AIPS) and the European Conference on Planning (ECP). ICAPS continues the traditional high standards of AIPS and ECP as an archival forum for new research in the field of automated planning and scheduling.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.056
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0060.003
Open science0.0020.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.9440.915

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.287
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractyes

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