Is this your app? Claim this page to add your own description, links and contact info. It's free. →
Version
6.5
Size
141.98 Mb
Updated
2 years ago
Released
18 Jun 2020
Description
Classic 2048 puzzle game redefined by AI.
Our 2048 is one of its own kind in the market. We leverage multiple algorithms to create an AI for the classic 2048 puzzle game.
* Redefined by AI *
We created an AI that takes advantage of multiple state-of-the-art algorithms, including Monte Carlo Tree Search (MCTS) [a], Expectimax [b], Iterative Deepening Depth-First Search (IDDFS) [c] and Reinforcement Learning [d].
(a) Monte Carlo Tree Search (MCTS) is a heuristic search algorithm introduced in 2006 for computer Go, and has been used in other games like chess, and of course this 2048 game. Monte Carlo Tree Search Algorithm chooses the best possible move from the current state of games tree (similar to IDDFS).
(b) Expectimax search is a variation of the minimax algorithm, with addition of "chance" nodes in the search tree. This technique is commonly used in games with undeterministic behavior, such as Minesweeper (random mine location), Pacman (random ghost move) and this 2048 game (random tile spawn position and its number value).
(c)Iterative Deepening depth-first search (IDDFS) is a search strategy in which a depth-limited version of DFS is run repeatedly with increasing depth limits. IDDFS is optimal like breadth-first search (BFS), but uses much less memory. This 2048 AI implementation assigns various heuristic scores (or penalties) on multiple features (e.g. empty cell count) to compute the optimal next move.
(d) Reinforcement learning is the training of ML models to yield an action (or decision) in an environment in order to maximize cumulative reward. This 2048 RL implementation has no hard-coded intelligence (i.e. no heuristic score based on human understanding of the game). There is no knowledge about what makes a good move, and the AI agent "figures it out" on its own as we train the model.
References:
[a] https://www.aaai.org/Papers/AIIDE/2008/AIIDE08-036.pdf
[b] http://www.jveness.info/publications/thesis.pdf
[c] https://cse.sc.edu/~MGV/csce580sp15/gradPres/korf_IDAStar_1985.pdf
[d] http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-8.pdf
Estimates
Availability
Devices
iPhone5s
iPadAir
iPadAirCellular
iPadMiniRetina
iPadMiniRetinaCellular
iPhone6
iPhone6Plus
iPadAir2
iPadAir2Cellular
iPadMini3
iPadMini3Cellular
iPodTouchSixthGen
iPhone6s
iPhone6sPlus
iPadMini4
iPadMini4Cellular
iPadPro
iPadProCellular
iPadPro97
iPadPro97Cellular
iPhoneSE
iPhone7
iPhone7Plus
iPad611
iPad612
iPad71
iPad72
iPad73
iPad74
iPhone8
iPhone8Plus
iPhoneX
iPad75
iPad76
iPhoneXS
iPhoneXSMax
iPhoneXR
iPad812
iPad834
iPad856
iPad878
iPadMini5
iPadMini5Cellular
iPadAir3
iPadAir3Cellular
iPodTouchSeventhGen
iPhone11
iPhone11Pro
iPadSeventhGen
iPadSeventhGenCellular
iPhone11ProMax
iPhoneSESecondGen
iPadProSecondGen
iPadProSecondGenCellular
iPadProFourthGen
iPadProFourthGenCellular
Pricing by country
| Country | Price |
|---|---|
| Canada | free |
| China | free |
| France | free |
| Germany | free |
| Italy | free |
| Netherlands | free |
| Portugal | free |
| Spain | free |
| UK | free |
| India | free |
| Japan | free |
| Korea, Republic Of | free |
| Poland | free |
| Russia | free |
| Turkey | free |
| USA | free |
| Ukraine | free |
