To download the repository:
git clone https://github.com/gabrielpreviato/mo416-2020s1-unicamp.git
Then you need to install the basic dependencies to run the project on your system:
cd mo416-2020s1-unicamp
pip install -r requirements.txt
You also need to fetch the datasets from the aima-data repository:
git submodule init
git submodule update
To implement and evaluate solutions based on search methods for the problem described in Project 1 PDF using the AIMA function library (https://github.com/aimacode/aima-python/blob/master/search.ipynb). This project aims in providing the solution implemeted with the following:
- Two search methods without information
- Two informed search methods with 2 distinct heuristics
- One local search method
The work consists of finding an adequate solution to the chosen problem, evaluating it according to: computational cost, completeness, optimality. Your are required to clearly define:
- How the problem was modeled
- Implementation specifics and restrictions
The solution video can be found at: https://www.youtube.com/watch?v=wTpuToSd8rc
Caroline Aparecida de Paula Silva (265188) contributed with the base code structure, the DFS, BFS and Greedy search algorithms, Manhattan Heuristic and graphical visualization.
Gabriel Previato de Andrade (172388) contributed with the A* search, the Wavefront Heuristic and the Simulated Annealing.
Thales Mateus Rodrigues Oliveira (148051) contributed with the A* search, the Ghost Avoidance Heuristic and Hill-Climbing.
Letícia Mara Berto (212069) contributed with the Mazes creation, experiments executions, graphical visualization and the solution video.
All of the above mentioned contributed in the analysis and final report.
This repository uses AIMA-Python modules (Copyright (c) 2016 aima-python contributors) distributed under the MIT license.