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FIRA MIDDLE-LEAGUE ROBOT SOCCER GAME STRATEGY

2007· article· en· W1992009003 on OpenAlexvenueno aff
Elmer A. Maravillas, Elmer P. Dadios

Bibliographic record

VenueControl and Intelligent Systems · 2007
Typearticle
Languageen
FieldComputer Science
TopicRobotic Path Planning Algorithms
Canadian institutionsnot available
Fundersnot available
KeywordsFuzzy logicComputer scienceRobotIdentifierMobile robotLeagueBall (mathematics)Task (project management)EngineeringControl engineeringControl (management)Control theory (sociology)Artificial intelligenceSimulationMathematicsComputer network

Abstract

fetched live from OpenAlex

The thrust of this paper is to design and implement a hierarchical control for the cooperative behavior of autonomous mobile robots using fuzzy logic systems with 5 vs. 5 Mirosot as the platform for its investigation. Our strategy is to identify the state of the game, offense or defense, using a fuzzy logic main-task-identifier. During offense a fuzzy control system is used by the ball handler to decide whether to shoot, pass, or just dribble the ball. To ensure success for these tasks, they are preceded by lane checking activity to assign a lane free of obstacles. Experimental results show that the proposed strategy is effective and robust.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.258
Teacher spread0.219 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations4
Published2007
Admission routes1
Has abstractyes

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