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README.md

Artificial Intelligence Nanodegree Program Resources

Classroom Exercises

1. Constraint Satisfaction Problems

In this exercise you will explore Constraint Satisfaction Problems in a Jupyter notebook and use a CSP solver to solve a variety of problems.

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2. Classical Search for PacMan (only in classroom)

Please DO NOT publish your work on this exercise.

In this exercise you will teach Pac-Man to search his world to complete the following tasks:

  • find a single obstacle
  • find multiple obstacles
  • find the fastest way to eat all the food in the map

3. Local Search Optimization

In this exercise, you'll implement several local search algorithms and test them on the Traveling Salesman Problem (TSP) between a few dozen US state capitals.

Projects

1. Sudoku Solver

In this project, you will extend the Sudoku-solving agent developed in the classroom lectures to solve diagonal Sudoku puzzles and implement a new constraint strategy called "naked twins". A diagonal Sudoku puzzle is identical to traditional Sudoku puzzles with the added constraint that the boxes on the two main diagonals of the board must also contain the digits 1-9 in each cell (just like the rows, columns, and 3x3 blocks).

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2. Classical Planning

This project is split between implementation and analysis. First you will combine symbolic logic and classical search to implement an agent that performs progression search to solve planning problems. Then you will experiment with different search algorithms and heuristics, and use the results to answer questions about designing planning systems.

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3. Game Playing

In this project you will choose an experiment with adversarial game-playing techniques like minimax, Monte Carlo tree search, opening books, and more. Your goal will be to build and evaluate the performance of your agent in a finite deterministic two player game of perfect information called Isolation.

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4. Part of Speech Tagger

In this notebook, you'll use the Pomegranate library to build a hidden Markov model for part of speech tagging with a universal tagset. Hidden Markov models have been able to achieve >96% tag accuracy with larger tagsets on realistic text corpora. Hidden Markov models have also been used for speech recognition and speech generation, machine translation, gene recognition for bioinformatics, and human gesture recognition for computer vision, and more.

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