An investigation into 2048 AI strategies

Philip Rodgers, John Levine

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

Abstract

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
PublisherIEEE
Number of pages2
ISBN (Electronic)9781479935475
DOIs
Publication statusPublished - 23 Oct 2014

Publication series

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

Keywords

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

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  • Cite this

    Rodgers, P., & Levine, J. (2014). An investigation into 2048 AI strategies. In 2014 IEEE Conference on Computational Intelligence and Games IEEE. https://doi.org/10.1109/CIG.2014.6932920