2048 (3x3, 4x4, 5x5) AI
ASO Keyword Dashboard
Tracking 97 keywords for 2048 (3x3, 4x4, 5x5) AI in Apple App Store
2048 (3x3, 4x4, 5x5) AI tracks 97 keywords (no keywords rank yet; 97 need traction). Key metrics: opportunity 47.9, difficulty 39.7.
Tracked keywords
97
0 ranked • 97 not ranking yet
Top 10 coverage
—
Best rank — • Latest leader —
Avg opportunity
47.9
Top keyword: best
Avg difficulty
39.7
Lower scores indicate easier wins
Opportunity leaders
- 85.9
best
Opportunity: 59.0 • Difficulty: 59.6 • Rank —
Competitors: 1,398
- 84.7
create
Opportunity: 59.0 • Difficulty: 60.6 • Rank —
Competitors: 1,155
- 87.6
like
Opportunity: 59.0 • Difficulty: 63.2 • Rank —
Competitors: 1,767
- 83.9
features
Opportunity: 59.0 • Difficulty: 60.4 • Rank —
Competitors: 1,060
- 74.7
puzzle
Opportunity: 58.0 • Difficulty: 50.1 • Rank —
Competitors: 447
Unranked opportunities
best
Opportunity: 59.0 • Difficulty: 59.6 • Competitors: 1,398
create
Opportunity: 59.0 • Difficulty: 60.6 • Competitors: 1,155
like
Opportunity: 59.0 • Difficulty: 63.2 • Competitors: 1,767
features
Opportunity: 59.0 • Difficulty: 60.4 • Competitors: 1,060
puzzle
Opportunity: 58.0 • Difficulty: 50.1 • Competitors: 447
High competition keywords
like
Total apps: 7,508 • Major competitors: 1,767
Latest rank: — • Difficulty: 63.2
best
Total apps: 6,283 • Major competitors: 1,398
Latest rank: — • Difficulty: 59.6
create
Total apps: 5,537 • Major competitors: 1,155
Latest rank: — • Difficulty: 60.6
features
Total apps: 5,134 • Major competitors: 1,060
Latest rank: — • Difficulty: 60.4
including
Total apps: 3,880 • Major competitors: 926
Latest rank: — • Difficulty: 62.0
All tracked keywords
Includes opportunity, difficulty, rankings and competitor benchmarks
| Major Competitors | |||||||
|---|---|---|---|---|---|---|---|
| best | 59 | 100 | 60 | 86 6,283 competing apps Median installs: 272,625 Avg rating: 4.6 | — | — | 1,398 major competitor apps |
| puzzle | 58 | 100 | 50 | 75 2,006 competing apps Median installs: 275,850 Avg rating: 4.7 | — | — | 447 major competitor apps |
| strategy | 58 | 100 | 46 | 67 937 competing apps Median installs: 252,800 Avg rating: 4.6 | — | — | 197 major competitor apps |
| action | 58 | 100 | 49 | 68 1,033 competing apps Median installs: 300,175 Avg rating: 4.6 | — | — | 251 major competitor apps |
| art | 58 | 100 | 47 | 69 1,082 competing apps Median installs: 239,212 Avg rating: 4.6 | — | — | 202 major competitor apps |
| version | 58 | 100 | 46 | 68 1,006 competing apps Median installs: 238,775 Avg rating: 4.6 | — | — | 182 major competitor apps |
| order | 20 | 100 | 61 | 73 1,756 competing apps Median installs: 288,212 Avg rating: 4.7 | — | — | 456 major competitor apps |
| environment | 57 | 100 | 38 | 59 397 competing apps Median installs: 218,075 Avg rating: 4.6 | — | — | 63 major competitor apps |
| classic | 58 | 100 | 51 | 74 1,817 competing apps Median installs: 292,025 Avg rating: 4.6 | — | — | 453 major competitor apps |
| used | 58 | 100 | 53 | 72 1,494 competing apps Median installs: 237,800 Avg rating: 4.6 | — | — | 279 major competitor apps |
| limited | 58 | 100 | 55 | 65 715 competing apps Median installs: 280,925 Avg rating: 4.6 | — | — | 171 major competitor apps |
| created | 58 | 100 | 47 | 67 953 competing apps Median installs: 213,175 Avg rating: 4.6 | — | — | 153 major competitor apps |
| multiple | 58 | 100 | 54 | 78 2,846 competing apps Median installs: 239,425 Avg rating: 4.6 | — | — | 550 major competitor apps |
| including | 58 | 100 | 62 | 81 3,880 competing apps Median installs: 285,400 Avg rating: 4.6 | — | — | 926 major competitor apps |
| score | 20 | 100 | 52 | 68 977 competing apps Median installs: 314,100 Avg rating: 4.7 | — | — | 264 major competitor apps |
| various | 58 | 100 | 50 | 75 2,177 competing apps Median installs: 229,325 Avg rating: 4.6 | — | — | 374 major competitor apps |
| tree | 57 | 100 | 33 | 46 112 competing apps Median installs: 295,300 Avg rating: 4.6 | — | — | 24 major competitor apps |
| search | 58 | 100 | 62 | 75 2,154 competing apps Median installs: 273,038 Avg rating: 4.6 | — | — | 531 major competitor apps |
| move | 58 | 100 | 51 | 71 1,366 competing apps Median installs: 278,150 Avg rating: 4.6 | — | — | 311 major competitor apps |
| understanding | 57 | 100 | 37 | 55 266 competing apps Median installs: 199,150 Avg rating: 4.6 | — | — | 39 major competitor apps |
| technique | 57 | 100 | 26 | 38 48 competing apps Median installs: 291,288 Avg rating: 4.7 | — | — | 10 major competitor apps |
| run | 58 | 100 | 47 | 70 1,204 competing apps Median installs: 261,300 Avg rating: 4.6 | — | — | 249 major competitor apps |
| create | 59 | 100 | 61 | 85 5,537 competing apps Median installs: 251,325 Avg rating: 4.6 | — | — | 1,155 major competitor apps |
| good | 58 | 100 | 52 | 71 1,413 competing apps Median installs: 276,475 Avg rating: 4.6 | — | — | 294 major competitor apps |
| ai | 58 | 100 | 53 | 71 1,363 competing apps Median installs: 253,250 Avg rating: 4.6 | — | — | 284 major competitor apps |
App Description
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 game's 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