Gain knowledge and insight on AI planning, model checking, and reinforcement learning (RL)
through research and experiment.
Task 1: Model the Grid World Maze in PAT
A grid world of maze is a 2-D matrix of points. Each point is either
• an ‘O’ for open space, or
• an ‘H’ for wall/obstacles, or
• an ‘S’ for the starting location, or
• a ‘G’ for the goal location.
You can choose integers in PAT to represent those types of points as long as you use them
Model a 10×10 grid world maze in PAT using the communicating sequent processes (CSP)
module. You can place the walls, the starting point, and the goal anywhere you wish.
Model the assertion for reaching the goal.
Use model checking to find the optimal path.
Task 2: Model the Grid World Maze for RL
See Workshop 10.
Task 3: Parameterise the Grid World
Automate the grid world modelling process by writing a piece of code to generate a grid world
of arbitrary size. Place the walls, the starting point, and the goal at random locations as long as
they do not overlap. Your code should take x and y as input parameters and generate a grid
world of size x by y.
Task 4: Translate the Grid World to a PAT Model and a Model for RL
PAT recognises multi-dimension arrays in a specific syntax. See the details in Section 18.104.22.168
in the link below.
You can model a 2-D array as a flattened 1-D array in PAT and access the elements using index
arithmetic coded by yourself.
For reinforcement learning, you only need to modify the grid.txt file accordingly.
The goal of this task is to take a grid world matrix as input, and generate two files:
• a .csp file of a PAT model, and
• a .txt file for RL.
Both files should contain the same grid world maze.
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