Análisis e implementación de un jugador automático de póquer
Bibliographic record
Abstract
Games have always been used as research domains in Computer Science \ndevelopment in general, as well as in Arti cial Intelligence (AI) in particular. \nIn this way, poker becomes a very interesting game, due to its common issues \nand characteristics with many other games and daily situations that are \nabout to be solved such as the huge amount of states that can be faced, the \nimperfect nature of the information that we handle or the random element \nthat needs to be taken into account. \nThe purpose of this paper is developing an agent capable of accomplish an \ne cient poker game in its Texas Hold'em limit variety to submit it to the \nAnnual Computer Poker Competition (ACPC), organized by the University \nof Alberta. \nTo get to it, a study about the state of the art has been deeply done. It has \nshown the di erent perspectives from which the development of an automatic \npoker agent can be faced, as well as the advantages and disadvantages \nthat each of them can hide. \nThe next step on the project development is focused on the analysis, the \ndesign and implementation of a system that allows players creation and the \nstudy of their results when confronted on several hands. This process of agent \nconstruction until getting to the de nitive one is gradual and recurrent, obtaining along the way a huge amount of intermediate players with many \ndi erent characteristics when playing. \nApart from the development and creation of new algorithms, the main purpose \nof making poker agents is, beyond doubt, being able to calculate its \ncapacities playing hands. The result section of this paper includes a large \namount of assortment tests that can be used to quantify the output of all of \nthe created players, also helping to determinate which of them is the best. \nThe aforementioned agent will be chosen to face a human player and, that \nway, achieve one of the main purposes as much from this paper as from the \ndevelopment of intelligent systems for games: defeating professional poker \nplayers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".