The Pac-Man project was originally developed for UC Berkeley's "Introduction to Artificial Intelligence" course, CS 188. They apply an array of AI techniques to playing Pac-Man.
This particular implementation of the aforementioned project was developed for the class of "Artificial Intelligence", in the Informatics Department, and was awarded with the maximum possible score by the auto-grader script (50/50).
The whole project was implemented with python2.
In this part, the Pac-Man agent finds paths through his maze world, both to reach a particular location and to collect food efficiently. General search algorithms were built and applied to Pac-Man scenarios.
A makefile is provided. You can use the command $ make for auto-grading and $ make clean to delete all generated .pyc files.
You can also check the full list of available commands in the commands.txt file.
Edited files:
- search.py : Where all the search algorithms reside.
- searchAgents.py : Where all the search-based agents reside.
Auto-grader evaluation:
Provisional grades
==================
Question q1: 3/3
Question q2: 3/3
Question q3: 3/3
Question q4: 3/3
Question q5: 3/3
Question q6: 3/3
Question q7: 5/4
Question q8: 3/3
------------------
Total: 26/25
In this part, agents for the classic version of Pac-Man, including ghosts, were designed.
Along the way, both minimax and expectimax search were implemented.
Efficient evaluation functions were also developed.
A makefile is provided. You can use the command $ make for auto-grading and $ make clean to delete all generated .pyc files.
Edited files:
- multiAgents.py : Where all the multi-agent search agents reside.
Auto-grader evaluation:
Provisional grades
==================
Question q1: 4/4
Question q2: 5/5
Question q3: 5/5
Question q4: 5/5
Question q5: 6/6
------------------
Total: 25/25
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