In this project, you will extend the Sudoku-solving agent developed in the classroom lectures to solve diagonal Sudoku puzzles. 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). You will also implement another strategy called "Naked Twins"
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.
In this project, you will experiment with adversarial search techniques by building an agent to play knights Isolation. Unlike the examples in the lecture where the players control tokens that move like chess queens, this version of Isolation gives each agent control over a single token that moves in L-shaped movements--like a knight in chess.
Isolation In the game Isolation, two players each control their own single token and alternate taking turns moving the token from one cell to another on a rectangular grid. Whenever a token occupies a cell, that cell becomes blocked for the remainder of the game. An open cell available for a token to move into is called a "liberty". The first player with no remaining liberties for their token loses the game, and their opponent is declared the winner.
In knights Isolation, tokens can move to any open cell that is 2-rows and 1-column or 2-columns and 1-row away from their current position on the board. On a blank board, this means that tokens have at most eight liberties surrounding their current location. Token movement is blocked at the edges of the board (the board does not wrap around the edges), however, tokens can "jump" blocked or occupied spaces (just like a knight in chess).
Finally, agents have a fixed time limit (150 milliseconds by default) to search for the best move and respond. The search will be automatically cut off after the time limit expires, and the active agent will forfeit the game if it has not chosen a move.
In this project, 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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Django
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Facebook
Microsoft
Google
Alibaba
D3
Tencent