Haskell WalkSat algorithm implementation

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In summary, the WalkSat algorithm takes a formula, a probability value, and a maximum number of flips as inputs and returns a model that satisfies the formula or failure. The algorithm randomly assigns truth values to atoms and then flips the value of an atom from an unsatisfied clause until it finds a satisfying model or reaches the maximum number of flips. The choice of which atom to flip is determined by two strategies: either randomly or by maximizing the number of satisfied clauses. The module also includes functions for extracting atoms from a clause or formula, and determining if a literal is present in a clause. The last function, flipSymbol, takes a model and atom and flips the truth value of the atom in the model.
  • #1
AkilMAI
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I need some help if possible with the following problem...The WalkSat algorithm takes a formula, a probability 0 =< p =< 1, and a boundary of maximum flips maxflips
and returns a model that satisfies the formula or failure. The algorithm begins by assigning truth values to
the atoms randomly, ie. generating a random model. Then it proceeds to flip the value of an atom from one
of the unsatisfied clauses until it is able to find a model that satisfies the formula or reaches the maximum
number of flips.
In order to select which atom from the currently selected clause to flip, it follows two strategies:
1. Either flip any atom randomly.
2. Or flip the atom that maximizes the number of satisfied clauses in the formula.
The choice to flip randomly is followed with probability p.
1.atomsClause :: Clause ! [Atom] This function must take a Clause and return the set of Atoms of
that Clause. Note that the set should not contain any duplicates.
2. atoms :: Formula![Atom] This function must take a Formula return the set of Atoms of a Formula.
Note that the set should not contain any duplicates.
3. isLiteral :: Literal ! Clause ! Bool This function returns True if the given Literal can be found
within the Clause.
4. flipSymbol :: Model ! Atom ! Model This function must take a Model and an Atom and flip the
truth value of the atom in the model.

Now I've done the first 3 I need some help with the last one, Here is the code:
module Algorithm where

import System.Random
import Data.Maybe
import Data.List

type Atom = String
type Literal = (Bool,Atom)
type Clause = [Literal]
type Formula = [Clause]
type Model = [(Atom, Bool)]
type Node = (Formula, ([Atom], Model))

atomsClause :: Clause -> [Atom]
atomsClause...

atoms :: Formula -> [Atom]
atoms...

isLiteral :: Literal -> Clause -> Bool
isLiteral .....

flipSymbol :: Model -> Atom -> Model
flipSymbol = undefined...this is the one
Thank you.
 
Last edited:
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  • #2
flipSymbol :: Model -> Atom -> ModelflipSymbol model atom = let newValue = not (fromJust (lookup atom model)) updatedModel = (atom, newValue):(filter ((/= atom).fst) model) in updatedModel
 

Related to Haskell WalkSat algorithm implementation

1. What is the WalkSat algorithm?

The WalkSat algorithm is a stochastic local search algorithm used for solving satisfiability problems. It is based on the idea of "walking" through the search space and making randomized changes to find a satisfying assignment for a given set of constraints.

2. How does the WalkSat algorithm work?

The WalkSat algorithm works by starting with a random assignment of values to the variables in a given set of constraints. It then iteratively makes random changes to the values of the variables, with the goal of finding a satisfying assignment that satisfies all of the constraints. If a satisfying assignment is found, the algorithm terminates. Otherwise, it continues to make changes until a predefined maximum number of iterations is reached.

3. What are the advantages of using the WalkSat algorithm?

The WalkSat algorithm has several advantages, including its ability to handle large and complex problem instances, its efficiency in finding satisfying assignments, and its ability to handle both satisfiable and unsatisfiable problems. It also has a relatively simple implementation compared to other SAT solvers.

4. Are there any limitations to the WalkSat algorithm?

While the WalkSat algorithm is effective for many problem instances, it does have some limitations. It may get stuck in local optima, meaning it cannot find a satisfying assignment even if one exists. It also relies heavily on randomized changes, so the quality of the solution it finds may vary depending on the initial assignment and the random choices made during the search process.

5. How is the WalkSat algorithm implemented in Haskell?

The WalkSat algorithm can be implemented in Haskell using a combination of functional programming techniques and data structures. The implementation typically involves representing the problem instance as a set of clauses and using recursive functions to make random changes and check for a satisfying assignment. Some common data structures used in the implementation include lists, arrays, and maps.

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