CS440_Assign1

Vacuum_world

Vacuum World 2x2 Environment

This project extends the classic Vacuum World problem to a 2x2 grid environment. The environment and agent models have been updated to accommodate this new setup, and four different types of agents have been implemented and tested: Random Agent, Table-Driven Agent, Simple Reflex Agent, and Model-Based Reflex Agent.

Setup

Prerequisites (for Mac)

Ensure that Python is installed on your machine. You may also need to install some dependencies. All required packages are listed in the requirements.txt file. Installation

Fork and Clone the repository:

https://github.com/aimacode/aima-python.git
cd aima-python

Set up a virtual environment and install dependencies: *note: running these commands in terminal rather than vscode terminal seems to work better. Also, restart VScode if certain packages like ipykernel or numpy don't seem to be installed even though already installed.

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

*note: make sure running the python environment in this virtual It should like this in the upper right corner of the vscode:

Also install ipykernel:

python3 -m pip install ipykernel -U --force-reinstall
python3 -m ipykernel --version

note:

Run commands in terminal outside of VSCode.
Check to make sure that you are in the correct environment.
Restart VSCode if needed to update installed packages.

Project Structure

The agents.py file contains the AI agent implementations, while the vacuum_world.py file models the 2x2 grid environment via Jupyter notebook.

Environment Description

The environment is now a 2x2 grid, represented as follows:

2x2 grid environment - agents.py

Change the environment to:

loc_A, loc_B = (0, 0), (0,1)
loc_C, loc_D = (1,0), (1, 1)

note: '0' = clean, '1' = dirty

Location A is now at top left, B is top right. Location C is bottom left, and D is bottom right.

Agent Descriptions

Random Agent

The RandomVacuumAgent selects actions randomly from the possible moves ('Up', 'Down', 'Left', 'Right', 'Suck', 'NoOp').

We added 'Up', and 'Down' commands, to be able to move in all diretion in a 2 x 2 environment.

Table-Driven Agent

The TableDrivenVacuumAgent uses the last known percept-action to determine its next action. Therefore, we needed only the 1 percept for the table to successfully run as expected.
Simple Reflex Agent

The SimpleReflexVacuumAgent acts based on condition-action rules:

Cleans a dirty cell.
Moves to adjacent cells according to a fixed sequence, which we determined would be a clockwise motion i.e. A -> B -> D -> C.

Model-Based Reflex Agent

The ModelBasedReflexAgent will attempt to clean its cell if it is dirty. If its adjacent cell is dirty, it will move to it to clean it. The agent maintains an internal model of the environment and updates it based on percepts. It chooses actions to clean all dirty cells efficiently.

Acknowledgements

We used https://github.com/aimacode/aima-python as a starting reference point.