https://github.com/rand-asswad/taquin
https://github.com/rand-asswad/taquin
15-puzzle a-star graph-search-algorithms iterative-deepening-search prolog swi-prolog
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/rand-asswad/taquin
- Owner: rand-asswad
- Created: 2019-10-24T17:38:06.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-12-03T23:44:54.000Z (over 6 years ago)
- Last Synced: 2025-02-25T13:29:35.931Z (over 1 year ago)
- Topics: 15-puzzle, a-star, graph-search-algorithms, iterative-deepening-search, prolog, swi-prolog
- Language: Prolog
- Homepage: https://rand-asswad.github.io/taquin/
- Size: 12.2 MB
- Stars: 1
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Taquin (8-puzzle)
The famous sliding tiles game implemented in **Prolog**
using A* graph search algorithm for N×M size puzzles.
## Usage
The code is implemented for [SWI Prolog](https://www.swi-prolog.org/) environment.
Run the following command in your shell (provided you have SWI Prolog installed):
```sh
swipl test_3x3.pl
```
You can also try `test_3x4.pl` or `test_4x4.pl`.
Then run in your prolog console the following query
```prolog
puzzle(Puzzle, Difficulty), testPuzzle(Puzzle, ?Algorithm, ?Heuristic).
```
- The `Heuristic` value can be ommitted, the program would call the most adapted
heuristic for the chosen algorithm (recommended).
- The `Algorithm` can be ommitted, the default value is `astar`.
## Project Structure
```
taquin
├── util
│ ├── core.pl....................core predicates
│ ├── hamming.pl.................hamming heuristic definition
│ ├── heuristic.pl...............heuristics wrapper predicates
│ ├── manhattan.pl...............manhattan heuristic definition
│ ├── moves.pl...................states adjacency
│ └── test.pl....................testing tools
├── taquin_astar.pl................A* algorithm
├── taquin_dfs.pl..................DFS-style algorithms
├── taquin.pl......................taquin solver wrapper
├── test_3x3.pl....................3×3 example puzzles
├── test_3x4.pl....................3×4 example puzzles
├── test_4x4.pl....................4×4 example puzzles
└── README.md......................quick usage guide
```
## Search Algorithms
- **Depth-First Search (DFS):** a simple implementation of DFS using prolog's search trees,
there is no optimality guaranty, probable overflow because of the lack of depth control.
- **Iterative Deepening DFS (ID-DFS):** analog to DFS with a given Depth, starts with an
underestimate of the optimal depth (an admissible heuristic)
and increments the depth successively until a solution is found.
Optimality is guarantied, solutions are almost always fast for 3×3 puzzles.
Nevertheless, for complex puzzles bigger than 3×3 the ID-DFS is as hopeless as DFS.
- **Greedy Search:** a simple greedy algorithms that chooses each step by minimizing a
heuristic (admissible or not), there is no optimality guaranty but the algorithm
is fast and finds a solution for all 3x3 puzzles with the `m3h` heuristic,
possible overflow for complex puzzles because of th lack of depth control.
- **A\* Search:** a rigourous A* implementation using a priority queue that is guarantied
to find an optimal solution with an admissible heuristic *manhattan* or *hamming*.
Is relatively slow, but is significantly faster and more performant in the case
of complex puzzles.
The code is well-documented and readable,
for more details the report is in [docs/book.pdf](https://rand-asswad.github.io/taquin/book.pdf)
written in French (as the project is part of Masters program at INSA Rouen).
## To-Do List
- Implement IDA\*
- Implement random puzzle generator
- Implement comparison (time and solution optimality-wise)