Team = 'Pyaneers'
Members = ['Andrew Baik', 'Jason Burns', 'Christopher Chapman', 'Alexander Stone']Gomokubot learns the game of gomoku using Google tensorflow.
win: five in a row
first move: black
mechanics: forcing moves: plays which line up four in a row, which force defensive moves by the other side, often for an extended time. Skilled players can read chains of these out to 40 moves. response: end forcing mechanics by playing interupting moves towards clusters of your own pieces that are already on the board, increading the chance for offensive opportunities.
- As a player, I like to be able to play against a computer
- As a player, I like to see computer learning
- As a player, I like to display the board on the web application while I play against computer
- As a developer, I like to access to play against computer
- As a developer, I like to transfer server data using json format
- As a developer, I like to store results to the database so that computer can learn from previous games
Fractalkine workflow; major level: squad, minor level: pairs, patch level: single. Conflict will be handled in the thunderdome, AKA the whiteboard.
Deployment: final product
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Master: successful development commits
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Developlemt: feature integration, code merging, and testing
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f_feature_specific_branches: feature development
ini concept:
training flow:
06SEP18: Design
Game logic (Controller)
- validation of the victory
- Validation move
RESTful endpoints (Server/APIView)
- Post (new game)
- Put (send new point)
- Get (front-end for user)
ML(tensorflow, itertools)
Front-end
- Bare minimum of yellow background with black outlines, black and white rocks. text block