/pandemic-ai

Primary LanguageJava

pandemic-ai

The project aims to create self learning artificial intelligence capable to beat the board game Pandemic. As i know nothing about neural network and deep learning, I will start from scratch : no library will be used to manage neural network.

Future releases

  • proper logging
  • comment code
  • provide use instruction
  • update README with mardown syntax
  • check MCTS algorithm implementation
  • write JMH benchmark for critical methods
  • optimize deck creation/duplication
  • optimize gameState duplication
  • junit tests for Pandemic.getMoves method
  • provide a more optimized Pandemic.getMoves method

Changelog 2019.06.28

  • Junit tests cover all pandemic package except material. JUnit tests to be added before any refactoring of the material package
  • Deck, PlayerDeck, PropagationDeck refactored
  • Benchmark deck critical operations (draw, duplicate)
  • add equivalent method to test logical equality between objects

2019.06.20

  • Deprecated observer pattern migrated to PropertyChangeListener
  • JUnit tests in progress

2019.06.18

  • Refactored project architecture
  • JUnit tests in progress
  • Add maven build process
  • Improved algorithm to find all valuable combinations of 4 actions
  • JMH benchmark

The steps I Building a decent Pandemic Board game

  1. Initializing the game (create architecture classes, useful methods) is its simplest way : no event card, no card limit, no character player
  2. Add log4j to track
  3. build the game mechanics : what the game do , effect of each action etc.
  4. Create the game engine : waiting for input to perform player actions and automatically performing game actions
  5. Add eventhandling to throw event when : player hand is full or when it must make an action -> DONE
  6. Build an interface to play pandemic and check everything runs smoothly

II Build a Monte Carlo Tree Search based AI and check if it works

  1. Undertsanding MCTS algorithm https://int8.io/monte-carlo-tree-search-beginners-guide/
  2. Implementing the algorithm
  3. Testing & debugging
  4. Optimization

III Build a neural network learning from the Monte Carlo Tree Search, Alphagozero's way

IV Standardize AI entry points and API to make it "game" independant

V Add events and characters role