https://github.com/fpclass/iterative-forward-search
A Haskell constraint satisfaction libary based on the Iterative Forward Search algorithm
https://github.com/fpclass/iterative-forward-search
Last synced: 8 months ago
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A Haskell constraint satisfaction libary based on the Iterative Forward Search algorithm
- Host: GitHub
- URL: https://github.com/fpclass/iterative-forward-search
- Owner: fpclass
- License: mit
- Created: 2020-07-22T13:23:22.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2021-07-29T10:00:44.000Z (almost 5 years ago)
- Last Synced: 2025-08-26T15:43:23.980Z (10 months ago)
- Language: Haskell
- Homepage:
- Size: 75.2 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: Changelog.md
- License: LICENSE
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README
# Iterative Forward Search
[](https://github.com/fpclass/iterative-forward-search/actions/workflows/haskell.yaml)
[](https://github.com/fpclass/iterative-forward-search/actions/workflows/stackage-nightly.yaml)
[](https://hackage.haskell.org/package/iterative-forward-search)
This library implements a contraint solver via the [iterative forward search algorithm](https://muller.unitime.org/lscs04.pdf). It also includes a helper module specifically for using the algorithm to timetable events.
## Usage
To use the CSP solver first create a `CSP` value which describes your CSP, for example
```haskell
csp :: CSP Solution
csp = MkCSP {
cspVariables = IS.fromList [1,2,3],
cspDomains = IM.fromList [(1, [1, 2, 3]), (2, [1, 2, 4]), (3, [4, 5, 6])],
cspConstraints = [ (IS.fromList [1, 2], \a -> IM.lookup 1 a != IM.lookup 2 a)
, (IS.fromList [2, 3], \a -> IM.lookup 2 a >= IM.lookup 3 a)
],
cspRandomCap = 30, -- 10 * (# of variables) is a reasonable default
cspTermination = defaultTermination
}
```
This example represents a CSP with 3 variables, `1`, `2` and `3`, where variable `1` has domain `[1, 2, 3]`, variable `2` has domain `[1, 2, 4]`, and variable `3` has domain `[4, 5, 6]`. The contraints are that variable `1` is not equal to variable `2`, and variable `2` is at least as big as variable `3`. It uses the default termination condition, and performs 30 iterations before we select variables randomly.
You can then find a solution simply by evaluating `ifs csp`, which will perform iterations till the given termination function returns a `Just` value.
### Timetabling
The `toCSP` function in `Data.IFS.Timetable` takes a mapping from slot IDs to intervals, a hashmap of event IDs to the person IDs involved, and a map of person IDs to the slots where they are unavailable and generates a CSP which can then be solved with `ifs`. For example:
```haskell
slotMap :: IntMap (Interval UTCTime)
slotMap = IM.fromList [(1, eventTime1), (2, eventTime2), (3, eventTime3)]
events :: HashMap Int [person]
events = HM.fromList [(1, [user1, user2]), (2, [user1])]
unavailability :: HashMap person (Set Int)
unavailability = HM.fromList [(user1, S.empty), (user2, S.fromList [1,3])]
csp :: CSP r
csp = toCSP slotMap events unavailability defaultTermination
```
This will generate a CSP that creates a mapping from the events 1 and 2 to the time slots 1, 2 and 3.
## Limitations
- Variables and values must be integers
- Only hard constraints are supported