SimpleMinMax 1.0

SimpleMinMax by Gerry Kichok is a software library that provides simple implementations of the Min-Max algorithm for various purposes such as game-playing, optimization, and decision-making.

SimpleMinMax uses a recursive search algorithm that explores the game tree or solution space by evaluating all possible moves or decisions at each level. It evaluates the outcomes based on a heuristic function and determines the best move or decision by backpropagating the scores.

SimpleMinMax supports multiple programming languages including but not limited to Python, Java, C++, and JavaScript. You can easily integrate SimpleMinMax into your projects regardless of which language you are using.

SimpleMinMax is primarily designed for solving turn-based games, but it can also be applied to many other types of problems that require decision-making in a sequential manner. It is commonly used in game theory, artificial intelligence, and optimization.

Yes, SimpleMinMax provides flexibility to customize the evaluation function according to your specific problem domain. You can define your own heuristics or scoring techniques to guide the algorithm's decision-making process.

No, SimpleMinMax assumes complete information about the game state. It does not handle games with hidden or probabilistic elements. If you require handling incomplete information, you may need to enhance SimpleMinMax or consider using other algorithms suitable for such scenarios.

No, SimpleMinMax provides a good approximation of the optimal solution but does not guarantee finding the absolute best move or decision. The quality of the solution depends on the accuracy of the evaluation function, the depth of the search, and the complexity of the problem.

The time complexity of SimpleMinMax is exponential in the game tree depth. It explores all possible moves recursively, so as the branching factor and depth increase, the algorithm's complexity grows rapidly. It's important to optimize the search space and apply pruning techniques to improve performance.

Yes, there are several alternative algorithms for game-playing and optimization problems such as Alpha-Beta Pruning, Monte Carlo Tree Search (MCTS), and Genetic Algorithms. The choice of algorithm depends on the specific problem requirements and constraints.

For more information about SimpleMinMax, you can refer to Gerry Kichok's official documentation, user guides, and example code available on his website or developer community forums. You can also explore related research papers and textbooks on topics like game theory and artificial intelligence.