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Record W2576239431

Action selection for hammer shots in curling

2016· article· en· W2576239431 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Joint Conference on Artificial Intelligence · 2016
Typearticle
Languageen
FieldComputer Science
TopicGaussian Processes and Bayesian Inference
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDelaunay triangulationCurlingHammerComputer scienceAction (physics)Selection (genetic algorithm)Artificial intelligenceState spaceTriangulationComputer visionMathematical optimizationAlgorithmMathematicsEngineeringGeometryStatisticsStructural engineeringMechanical engineeringPhysics
DOInot available

Abstract

fetched live from OpenAlex

Curling is an adversarial two-player game with a continuous state and action space, and stochastic transitions. This paper focuses on one aspect of the full game, namely, finding the optimal hammer shot, which is the last action taken before a score is tallied. We survey existing methods for finding an optimal action in a continuous, low-dimensional space with stochastic outcomes, and adapt a method based on Delaunay triangulation to our application. Experiments using our curling physics simulator show that the adapted Delaunay triangulation's shot selection outperforms other algorithms, and with some caveats, exceeds Olympic-level human performance.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.174
GPT teacher head0.359
Teacher spread0.185 · 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