Game Playing: Proposing and Developing Queen Challenge Puzzle Game from 1 to 25 Levels
Bibliographic record
Abstract
In recent decades, massive improvements in graphic sophistication have begun to produce declining returns. The creative focus in game development has shifted to artificial intelligence. The queens’ task game is part of a sequence of popular games. It is the challenge of putting n chess queens on a game board such that no two queens are threatening each other. The plan does not involve two queens sharing the same row, column or diagonal. Each column contains exactly one queen, each row contains exactly one queen, and each diagonal contains exactly one queen. For every level in the game, there are many ways to solve it. For example, there are 92 solutions to the 8×8 problem. There are many levels in the literature, but each level should be downloaded separately. Thus, it causes a lot of difficulties for players, and they should download each level to complete the challenge. This will lead to more time and effort being spent by the players, and the cost of each level will cost the players more and more. As a result, the number of players who want to play this game will decrease. The aim of this paper is to incorporate a number of levels in order to save time, money and effort by downloading each level separately. This paper also aims to develop the proposed prototype and display all the solutions while playing a puzzle game at any level. The proposed game was tested by a questionnaire-based empirical study. Descriptive statistics on the questions revealed that the players had achieved the objectives of the game by applying their skills and knowledge and that the players had positive emotions about the effectiveness of the proposed game.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".