An investigation into 2048 AI strategies

Philip Rodgers, John Levine

Research output: Chapter in Book/Report/Conference proceedingConference contribution


2048 is a recent stochastic single player game, originally written in JavaScript for playing in a web browser but now largely played on mobile devices [1]. This paper discusses the applicability of Monte-Carlo Tree-Search (MCTS) to the problem, and also Averaged Depth Limited Search (ADLS). While MCTS plays reasonably well for a player with no domain knowledge, the ADLS player fares much better given an evaluation function that rewards board properties. Attempts to guide the roll-outs of MCTS using an evaluation function proved fruitless.
Original languageEnglish
Title of host publication2014 IEEE Conference on Computational Intelligence and Games
Number of pages2
ISBN (Electronic)9781479935475
Publication statusPublished - 23 Oct 2014

Publication series

ISSN (Print)2325-4270
ISSN (Electronic)2325-4289


  • artificial intelligence
  • Monte Carlo methods
  • tree searching
  • computer games


Dive into the research topics of 'An investigation into 2048 AI strategies'. Together they form a unique fingerprint.

Cite this