CSE571-Project Tools for Sequential Decision-Making Solved

Planning Domain Definition Language (PDDL) strives to standardize representations of sequential decision-making problems. Using a standardized language supports successful collaboration across project teams to solve problems efficiently. The purpose of this project is to challenge students to analyze a domain file and determine how to modify it to get the AI agent to achieve the desired goal. 

 

Technology Requirements:  

●       Personal computer with reliable WiFi

●       It is recommended to use the Fast-Downward Planne​ r​ to verify your solution, but it is not required.  

 

Project Strategy Questions:  

This project is based on ​Pac-Man​, a very famous arcade game popularized in the 1980s and often noted as one of the most famous video games of all time.  

Maze 1
  

Question 1
 

Review Maze 1, a 4X4 maze with locations defined as XiYi. It shows Pac-Man’s current location as ​Start​ and his goal location as ​Goal​. There are no walls or food pellets in Maze 1. Those will be added to other mazes as you continue progressing through the project.  

 

Two files are required to represent any environment that you want to solve using PDDL:

 

1.     Problem file​ - which would contain information about your objects in the environment, your initial state and a goal state.

2.     Domain file​ - which would contain list of predicates in use, types for objects (similar to data type in any programming language) and actions.

A ​partially filled problem file (q1_problem.pddl)​ and a ​complete domain file (q1_domain.pddl) representing Maze 1 is provided for your testing.  

Task: Write a goal condition so that solution plans will move Pac-Man to the goal location shown in Maze1.  (To verify your solution, you can add the goal condition inside the partially filled problem file and run it using the planner.) ​Refer to the project area in the course to view and select answers.  

Maze 2
  

 

Question 2
 

Review Maze 2. Maze 2 shows Pac-Man’s start location as Pacman​             and goal location as ​       Goal​     .​ Now, we will block the location X1Y2 such that the Pac-Man cannot move into this location.  

 

Which propositions do you need to remove from the Initial State (:init) of the problem file (q2_problem.pddl) to incorporate this information (Pac-Man should not move into the blocked location)? (To verify your solution, you must use this updated q2_problem.pddl as problem file, q2_domain.pddl as domain file and run it using the planner). ​Refer to the project area in the course to view and select answers.  

 

Question 3
After all the hard work we have put Pac-Man through, he is hungry, so food pellets will be added to Maze 2. Suppose there are food pellets at the locations X1Y4, X2Y1, X3Y3 and X4Y4. You will need to add a predicate ​dot_at (dot-at loc_x98y98)​ indicating whether or not a dot is at a location in the initial state (you can edit the initial state inside the problem file q3_problem.pddl and to verify your solution, you would first have to solve Question 4).  

 

Which option incorporates the scenario in this question? ​Refer to the project area in the course to view and select answers.  

 

Question 4
Similarly to Question 3, your task will be to help Pac-Man eat all the food pellets present in locations X1Y4, X2Y1, X3Y3 and X4Y4. You will need to add a predicate ​eaten (eaten loc_x98y98)​ in the goal condition (you can add the goal condition inside the problem file q3_problem.pddl) indicating that the food at a particular location is eaten by Pac-Man.  (To verify your solution for this question and the previous question, you must use the updated q3_problem.pddl as problem file, q3_domain.pddl as domain file and run it using the planner).

Which option, when added as a goal condition, will make Pac-Man eat ​all​ the food pellets? Refer to the project area in the course to view and select answers.  

Question 5
We made Pac-Man go through a lot of trouble and because of that he is feeling very sleepy. Now, if Pac-Man tries to make a move, he will succeed with only 70% probability and will stay at the same location with 30% probability. Given this condition, can you figure out how the “effect” for action “moveLeft” will change? ​Refer to the project area in the course to view and select answers.