#Minesweeper code

Mastermind is a puzzle in which a hidden code of length в„“ and made with Оє colors has to be discovered via making guesses of the code and receiving hints that express the distance from the guess to the code, in terms of number of symbols in the right position and with the right color. Thus, such an AI solver could contribute to classifying single-agent stochastic puzzles and establishing the boundary of the puzzle-solving and game-playing paradigm. The experimental simulation of various configurations and the AI solver with PAFG strategy yielded a high-level winning rate of 96.4%, 86.3%, and 45.6% for the 9Г—9|10, 16Г—16|40, and 16Г—30|99 Minesweeper board configuration, which is comparable to the state of the art study. The last two strategies explore the beginning and ways to determine hidden puzzle states to enhance the winning rate of the AI solver. The first two strategies take advantage of knowledge-based rules and linear system transformation (Gauss-Jordan elimination algorithms) to determine the probability of making a move independently. Secondly, this study proposes an artificial intelligence (AI) solver based on the obtained information on the board, called the вЂPAFGвЂ™ strategy, which stands for the primary reasoning, the advanced reasoning, the first action strategy, and the guessing strategy. Firstly, a single-agent stochastic puzzle definition is established via Minesweeper testbed, a well-known puzzle synonymous with Microsoft Windows. The contribution of the study is twofold. However, although previous researches focused on the puzzleвЂ™s complexity, strategy, and solving automatically, few studies worked on sorting out puzzles from a solvability way related to the stochastic elements among the solving process. People have enjoyed solving puzzles for decades because of the challenge and the satisfaction derived from solving problems. Results from these experiments show that the proposed method of MDP based classification model and deep Q-learning overall is the best methods in terms of accuracy for games with given mine densities. The overall results have been analyzed for different kinds of Minesweeper games and their accuracies have been recorded. We also propose a new rewarding method for applying a modified deep Q-learning for better accuracy and versatile learning in the Minesweeper game. We train the classification model on sparse data with results from CSP formulation. Using MDP, we implement machine learning methods on these heuristics. We propose a new method named as dependents from the independent set using deterministic solution search (DSScsp) for the faster enumeration of all solutions of a CSP based Minesweeper game and improve the results by introducing heuristics. The present work models this game as Constraint Satisfaction Problem (CSP) and Markov Decision Process (MDP). As an exemplary NP-complete problem, it is a major area of research employing various artificial intelligence paradigms. Minesweeper is a popular spatial-based decision-making game that works with incomplete information.