Greg Surma - iOS, AI, Machine Learning, Swit, Python, Objective-C
Repeat games Start a new game An investigation into 2048 AI strategies. ... Rodgers and Levine  discuss application of Monte-Carlo Tree-Search and Averaged Depth Limited Search in 2048 solving. Xiao  implemented an ... Quantum Monte Carlo Tree Search for Artificial Intelligence. The main engine behind the success of modern artificial intelligence (e.g. AlphaGo etc.) is the algorithms that combines deep machine learning approaches with the technique called Monte Carlo tree search. In Quantum Monte Carlo Tree Search (QMCTS) the algorithm searches for possible ... An implementation of the 2048 puzzle game using an AI agent that utilities Monte Carlo Tree Search, Mean Average-based Simulation and a hybrid model encompassing both techniques. As a stand-alone algorithm Monte Carlo did not perform very well. Scores were increased significantly when using a simulation-based approach. Number one Othello site on internet. All information, rules, tips, setups, strategies for Othello. simple Monte Carlo (depth 3) 14586 14295 6592 2048 1024 expectimax (depth 2) 14398 14241 6717 2048 1024 expectimax (depth 3) 27132 25270 10429 4096 2048 10-sample expectimax (depth 2) 14496 14623 7096 4096 1024 10 -sample expectimax (depth 3) 27052 24883 10106 4096 2048 •Simple Monte Carlo can achieve the 2048 tile.
Monte Carlo method I became interested in the idea of AI for this game, in which there is no hard-coded intelligence (that is, there are no heuristics, scoring, etc.). The AI must "know" only the rules of the game and "understand" the game. Greg Surma - iOS, AI, Machine Learning, Swit, Python, Objective-C
The game tree in Monte Carlo tree search grows asymmetrically as the method concentrates on the more promising subtrees. Thus [dubious – discuss] it achieves better results than classical algorithms in games with a high branching factor. Moreover, Monte Carlo tree search can be interrupted at any time yielding the most promising move already ... Number one Othello site on internet. All information, rules, tips, setups, strategies for Othello. describe a progression of Monte Carlo simulation and search methods providing modest, achievable goals for introductory CS1 through advanced AI students. Game Description The 2048 game is played on a 4-by-4 square grid that is partially filled with tiles labeled with powers of 2. The primary goal of the game is to merge randomly I've read mcts.ai/ website and many papers about it, including one that shows some results about the successfulness of applying Monte Carlo Search with UCB in the AI for a Magic cards game, that is more or less what I need to do, however I'm having some trouble trying to understand some points and how to apply it so solve what I need. I developed a 2048 AI using expectimax optimization, instead of the minimax search used by @ovolve's algorithm. The AI simply performs maximization over all possible moves, followed by expectation over all possible tile spawns (weighted by the probability of the tiles, i.e. 10% for a 4 and 90% for a 2).