本学期上的《人工智能导论》课部分采用了Berkeley的CS188课程内容。今天整理了Project1:Search的实验报告,供大家学习交流。实验的instruction见该网址:https://inst.eecs.berkeley.edu/~cs188/sp20/project2/实验的文件见:https://inst.eecs.berkeley.edu/~cs188/sp20/a
computeActionFromValues(State)根据self.values给出的值函数计算最佳操作。 computeQValueFromValues(state,action)返回self.values的value函数给出的(state,action)计算的Q-value值。 这些数量都显示在图形用户界面中:值是方格中的数字, Q-value是四分之一方格中的数字,策略是从每个方格中的向外箭头。 重要提示:使...
Project 5: TrackingImplementation of exact and approximante inference using Bayes Nets.Provisional grades === Question q1: 2/2 Question q2: 3/3 Question q3: 3/3 Question q4: 2/2 Question q5: 2/2 Question q6: 3/3 Question q7: 3/3 Question q8: 1/1 Question q9: 3/3 Question q10:...
第一个函数getAction并不需要去修改它,是一个获取下一步行动的函数。而行动则是根据一定分数来评估,这个分数需要调用函数evaluationFunction来获取。最初的evaluationFunction只有基础的功能,需要自己来编写评估函数,返回一个分数。这个分数的设计方式就是计算当前位置与所有食物的曼哈顿距离,取反,和所有鬼魂的距离向乘,加...
> > Q1. [20 pts] Search 7 changes: 4 additions & 3 deletions 7 project/README.md Original file line numberDiff line numberDiff line change @@ -7,16 +7,17 @@ dg-publish: true # The Pac-Man Projects 这里是CS188 的 project [The Pac-Man Projects](https://inst.eecs.berkeley.edu...
python autograder.py -q q1 To run it without graphics, use: python autograder.py -q q1 --no-graphics Don’t spend too much time on this question, though, as the meat of the project lies ahead. Question 2 (5 points): Minimax Now you will write an adversarial search agent in the...
As in Project 0, this project includes an autograder for you to grade your answers on your machine. This can be run with the command: 1 python autograder.py See the autograder tutorial in Project 0 for more information about using the autograder. ...
第一个函数getAction并不需要去修改它,是一个获取下一步行动的函数。 而行动则是根据一定分数来评估,这个分数需要调用函数evaluationFunction来获取。最初的Function只有基础的功能,需要自己来编写评估函数,返回一个分数。 这个分数的设计方式就是计算当前位置与所有食物的曼哈顿距离,取反,和所有鬼魂的距离向乘,加上...
python autograder.py -q q1 --no-graphics Don’t spend too much time on this question, though, as the meat of the project lies ahead. Question 2 (5 points): Minimax Now you will write an adversarial search agent in the provided MinimaxAgent class stub in multiAgents.py. Your minimax ...
本学期上的《人工智能导论》课部分采用了Berkeley的CS188课程内容。今天整理了Project1:Search的实验报告,供大家学习交流。实验的instruction见该网址:https://inst.eecs.berkeley.edu/~cs188/sp20/project2/实验的文件见:https://inst.eecs.berkeley.edu/~cs188/sp20/a