A bi-objective algorithm that optimizes time and money is developed for the modal choices in the San Francisco Bay Area region. Here, the users choose between driving and BART during the peak and non-peak hours. The model also estimates the cost and emissions of their choices.
This was developed as part of the Udacity's Artificial Intelligence nanodegree program. Here, I implement Minimax algorithm with and without alpha-beta pruning to identify the next move for the computer player.
A "tournament" was given as the part of the exercise to test the computer player with different types of adversaries. I developed three kinds of heuristics for the computer player. See here for the analysis of them and how they can differ to predict the end-game.