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所在平台: Udemy |
课程主页: https://www.udemy.com/course/artificial-intelligence-games-in-java/
课程评论：没有评论
本课程“人工智能 I：元启发式算法与 Java 游戏”旨在教授人工智能的基础概念。 课程内容涵盖以下几个主要部分： **1. 路径搜索算法：** * **广度优先搜索 (BFS)：** 介绍 BFS 算法及其在人工智能中应用图算法的重要性。 * **深度优先搜索 (DFS)：** 讲解 DFS 算法、迭代与递归实现、内存可视化以及在迷宫逃脱等应用中的使用。 * **迭代加深深度优先搜索 (IDDFS)：** 介绍 IDDFS 算法。 * **A\* 搜索算法：** 详细讲解 A\* 搜索算法，对比 Dijkstra 算法，并介绍启发式函数（如曼哈顿距离和欧几里得距离）。 **2. 优化方法：** * **基础优化算法：** 介绍暴力搜索、爬山算法等基本优化技术。 **3. 元启发式算法：** * **模拟退火：** 讲解模拟退火算法，如何寻找函数极值，以及解决组合优化问题（如旅行商问题 TSP）。 * **遗传算法：** 介绍遗传算法、人工进化与自然选择、交叉与变异，以及解决背包问题。 * **粒子群优化 (PSO)：** 讲解群体智能和粒子群优化算法。 **4. 游戏与博弈树：** * **博弈树：** 介绍博弈树的概念及其构建。 * **Minimax 算法与游戏引擎：** 讲解 Minimax 算法、博弈树的局限性以及 Alpha-Beta 剪枝技术在国际象棋等问题中的应用。 * **Tic Tac Toe (井字棋) 与 Minimax：** 实现井字棋游戏，并应用 Minimax 算法。 总体而言，本课程从基础图算法开始，深入到局部搜索、启发式与元启发式算法，最终探讨了游戏 AI 的核心技术，并且会通过实现井字棋游戏来巩固学习。
This course is about the fundamental concepts of artificial intelligence. This topic is getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detecting cancer for example. We may construct algorithms that can have a very good guess about stock price movement in the market. - PATHFINDING ALGORITHMS -Section 1 - Breadth-First Search (BFS)what is breadth-first search algorithmwhy to use graph algorithms in AISection 2 - Depth-First Search (DFS)what is depth-first search algorithmimplementation with iteration and with recursiondepth-first search stack memory visualizationmaze escape applicationSection 3 - Iterative Deepening Depth-First Search (IDDFS)what is iterative deepening depth-first search algorithmSection 4 - A* Search Algorithmwhat is A* search algorithmwhat is the difference between Dijkstra's algorithm and A* searchwhat is a heuristicManhattan distance and Euclidean distance - OPTIMIZATION -Section 5 - Optimization Approachesbasic optimization algorithmsbrute-force searchhill climbing algorithm- META-HEURISTICS -Section 6 - Simulated Annealingwhat is simulated annealinghow to find the extremum of functionshow to solve combinatorial optimization problemstravelling salesman problem (TSP)Section 7 - Genetic Algorithmswhat are genetic algorithmsartificial evolution and natural selectioncrossover and mutationsolving the knapsack problemSection 8 - Particle Swarm Optimization (PSO)what is swarm intelligencewhat is the Particle Swarm Optimization algorithm- GAMES AND GAME TREES -Section 9 - Game Treeswhat are game treeshow to construct game treesSection 10 - Minimax Algorithm and Game Engineswhat is the minimax algorithmwhat is the problem with game trees?using the alpha-beta pruning approachchess problemSection 11 - Tic Tac Toe with MinimaxTic Tac Toe game and its implementationusing minimax algorithmIn the first chapter we are going to talk about the basic graph algorithms. Several advanced algorithms can be solved with the help of graphs, so as far as I am concerned these algorithms are the first steps.Second chapter is about local search: finding minimum and maximum or global optimum in the main. These searches are used frequently when we use regression for example and want to find the parameters for the fit. We will consider basic concepts as well as the more advanced algorithms: heuristics and meta-heuristics.The last topic will be about minimax algorithm and how to use this technique in games such as chess or tic-tac-toe, how to build and construct a game tree, how to analyze these kinds of tree like structures and so on. We will implement the tic-tac-toe game together in the end.Thanks for joining the course, let's get started!
