DEEP RED An Intelligent Approach to Chinese Checkers

The Problem Create an Artificial Intelligence that can successfully play Chinese Checkers, with a random board.

Strategy Overview Complicated games like chess and the modified game of Chinese Checkers have complicated strategies We will likely not be able to experience all the nuances of this strategy in the class period We want to use Machine Learning, so that the machine might learn complicated winning strategies

Reading / External Sources We will be using the sections on machine learning (18-20) in R+N Possibly other data sources, depending on complexity of theory behind implementation

I/O Input o (0, initialization of board) o (1, move request) o (2, enemy move) o (3, error number) o (4, end game)

System Architecture We will use as data sources the games that both the heuristic and learning bot play

Data Sources Our learning agent will provide data collected over the course of design If other data sources are used, we may incorporate them, if they pass a sanity check

Evaluation Plans Initially our AI will compete with a heuristic bot After it learns enough to best the heuristic bot most of the time (90%, optimistically), we will train it against itself At all times, the data collected will go towards the learning algorithm

I/O Input o (0, initialization of board) o (1, move request) o (2, enemy move) o (3, error number) o (4, end game)