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   • constraint satisfaction in java

     Top Answer

     Answered Sep 16, 2012 · 1 votes

     You could perhaps do this through the use of some helper methods from the google commons collect library. It would look something like this:

     I'm assuming that the tuples (0,0,0) etc are tuples of the input to the constraint, (x0, x1, x2) for constraint1, (x2, x4) for constraint2 etc.

     So, for constraint1, first we fill a list with all possible combinations:

     final List<int[]> allCombos = new ArrayList<int[]>(); for (int i = 0; i < 3; i++) { for (int j = 0; j < 3; j++) { for (int k = 0; k < 3; k++) { allCombos.add(new int[] {i, j, k}); } } } for (final int[] i : allCombos) { System.out.println(i[0] + ", " + i[1] + ", " + i[2]); }

     Next, we want to filter so we'll be left with the tuples that are allowed by constraint1:

     final List<int[]> constraint1 = ImmutableList.copyOf(Iterables.filter(allCombos, new Predicate<int[]>() { @Override public boolean apply(@Nullable final int[] input) { return input[0] + input[1] > input[2]; } })); for (final int[] i : constraint1) { System.out.println(i[0] + ", " + i[1] + ", " + i[2]); }

     This might need a little explanation.

     ImmutableList.copyOf is a method that creates a copy of a given list. To this method, we pass the result of Iterables.filter(), which takes a list (the input to be filtered), and a Predicate, which has an overridden method apply(), where you decide which element of the input list that are supposed to be part of the result list. Here, we basically just code the constraint itself, and the cases where the apply method returns true will be part of the filtered list. (I've chosen to represent the tuples as an array, you could use the filter-strategy with any tuple-representation..)

     The result of the last printouts (the filtered list) will be:

     0, 1, 00, 2, 00, 2, 11, 0, 01, 1, 01, 1, 11, 2, 01, 2, 11, 2, 22, 0, 02, 0, 12, 1, 02, 1, 12, 1, 22, 2, 02, 2, 12, 2, 2

     I'll leave it up to you to do the same for the other constraints..

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   • understanding constraint satisfaction problem: map coloring algorithm

     Top Answer

     Answered Oct 07, 2018 · 0 votes

     So after rehashing some college literature (Peter Norvig's Artificial Intelligence: A Modern Approach), it turns out the problem in your hands is the application of Recursive Backtracking as a way to find a solution for the Graph Coloring Problem, which is also called Map Coloring (given its history to solve the problem of minimize colors needed to draw a map). Replacing each country in a map for a node and their borders with edges will give you a graph where we can apply recursive backtracking to find a solution.

     Recursive backtracking will descend the graph nodes as a depth-first tree search, checking at each node for whether a color can be used. If not, it tries the next color, if yes, then it tries the next unvisited adjacent node. If for a given node no color satisfies the condition, it will step back (backtrack) and move on to a sibling (or the parent's sibling if no siblings for that node).

     So,

     I assume that since assignment is inputted as an empty dictionary {}, that this will return the solution, that is, the dictionary that contains states and their colors ... Would it be something like checking the size of the dictionary against the number of states?

     Yes and yes. Once the dictionary contains all the nodes of the graph with a color, you'll have a solution.

     The input csp class contains a list of states, I assume this could just be var equal to popping off a value in the list?

     That pseudocode syntax is confusing but the general idea is that you'll have a way to find out a node of the graph that hasn't been colored yet. One simply way is to return a node from the dictionary that doesn't have a value assigned to it. So if I understand the syntax correctly, var would store a node. VARIABLES[csp] seems to me like a representation of the list of nodes inside your CSP structure.

     I'm not sure what the parameters (VARIABLES[csp], assignment, csp) are doing, given my input

     The assignment parameter is a dictionary containing the nodes evaluated so far (and the future solution), as mentioned above, and csp is the structure containing a,b and c.

     Again, confused on what the inputs of (var, assignment, csp) are doing exactly. But I assume that it'll go through each value (neighbor) in dictionary of the state?

     ORDER-DOMAIN-VALUES appears to be a function which will return the ordered set of colors in your CSP structure. The FOR loop will iterate over each color so that they're tested to satisfy the problem at that level.

     if value is consistent with assignment given CONSTRAINT[csp] then-

     Here, what you're doing is testing the constraint with that value, to ensure it's true. In this case you want to check that any nodes adjacent to that node does not have that color already. If an adjacent node has that color, skip the IF and iterate the for loop to try the next color.

     If no adjacent nodes have that color, then enter the IF body and add that node var with color value to the assigment dictionary (I believe {var = value} is a tuple representation, which I would write {var,value}, but oh well). Then call the function recursive backtracking again, recursively. If the recursive call returns non-failure, return its results (it means the solution has been found).

     If it returns a failure (meaning, it tried all the colors and all of them happened to be used by another adjacent node), then remove that node ({var,value}) from the assignment (solution) array and move on to the next color. If all colors have been exausted, return failure.

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   • Constraint Satisfaction Problems (CSP) in Artificial Intelligence

     Finding a solution that meets a set of constraints is the goal of constraint satisfaction problems (CSPs), a type of AI issue. Finding values for a group of variables that fulfill a set of restrictions or rules is the aim of constraint satisfaction problems. For tasks including resource allocation, planning, scheduling, and decision-making, CSPs are frequently employed in AI.

     There are mainly three basic components in the constraint satisfaction problem:

     Variables:    The things that need to be determined are variables. Variables in a CSP are the objects that must have values assigned to them in order to satisfy a particular set of constraints. Boolean, integer, and categorical variables are just a few examples of the various types of variables Variables, for instance, could stand in for the many puzzle cells that need to be filled with numbers in a sudoku puzzle.

     Domains:     The range of potential values that a variable can have is represented by domains. Depending on the issue, a domain may be finite or limitless. For instance, in Sudoku, the set of numbers from 1 to 9 can serve as the domain of...

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   • Backtracking search for constraint satisfaction problem implementation

     Top Answer

     Answered Nov 10, 2018 · 0 votes

     The first step is to create a knowledge model for the domain. This can be done with a domain specific language (DSL). In the DSL syntax a possible solution to the problem is formulated. The wanted side effect of a domain specific language is, that a grammar has to be created which can parse the language. This grammar can be used in the reverse order which is called a "generative grammar". The aim is to include as much domain knowledge as possible in the DSL which makes it easier for the solver to test out all states.

     Unfortunately, the question has only a few information about the domain itself. According to the description there are three variables which can be on or off. This would generate a possible statespace of 2x2x2=8 which seems a bit too easy for a domain, because the solver is done if he tested out all 8 states. So i would guess, that the problem is bit harder but not explained in the description. Nevertheless, the first step is to convert the problem into a language which can be parsed by a grammar.

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   • Solve Tic Tac Toe with Constraint satisfaction problem (CSP)

     Top Answer

     Answered Dec 21, 2022 · 1 votes

     As far as I understand your question I hope this answer should work :) Hope SO...

     def CSP(board, col, row): if Is_board_full(board): return True-- for i in range(row): if is_valid(i , col, board): board[i][col] = 1 if CSP(board, col+1, row) == True: return True board[i][col] = 1 return False

     With a new function

     def Is_board_full(board):# Is the whole board full?for i in range(0, 3): for j in range(0, 3): # There's an empty field, we continue the game if (board[i][j] == 0): return Falsereturn True

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2. www.geeksforgeeks.org › constraint-satisfactionConstraint Satisfaction Problems (CSP) in Artificial ...

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   3 days ago · Finding a solution that meets a set of constraints is the goal of constraint satisfaction problems (CSPs), a type of AI issue. Finding values for a group of variables that fulfill a set of restrictions or rules is the aim of constraint satisfaction problems.

3. www.nature.com › articles › s42256/024/00833-7Distributed constrained combinatorial optimization leveraging ...

   www.nature.com › articles › s42256/024/00833-7

   20 hours ago · Scalable addressing of high-dimensional constrained combinatorial optimization problems is a challenge that arises in several science and engineering disciplines. Recent work introduced novel ...

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   What is a constraint satisfaction problem?

   • Finding values for a group of variables that fulfill a set of restrictions or rules is the aim of constraint satisfaction problems. For tasks including resource allocation, planning, scheduling, and decision-making, CSPs are frequently employed in AI. Variables: The things that need to be determined are variables.

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   • Constraint Satisfaction Problem in Artificial Intelligence A Constraint Satisfaction Problem (CSP) is a program that requires its solution within some limitations or conditions that is called Constraints. It consists of: 1. A finite set of variables which store the solution V={V1,V2,V3,…,Vn}2.

   Constraint Satisfaction Problem in Artificial Intelligence

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5. link.springer.com › article › 10Algebraic Global Gadgetry for Surjective Constraint Satisfaction

   link.springer.com › article › 10

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   1 day ago · The constraint satisfaction problem (CSP) on a finite relational structure B is to decide, given a set of constraints on variables where the relations come from B, whether or not there is an assignment to the variables satisfying all of the constraints; the surjective CSP is the variant where one decides the existence of a surjective satisfying assignment onto the universe of B. We present an ...

6. epubs.siam.org › doi › 10Complexity Classifications of Boolean Constraint Satisfaction ...

   epubs.siam.org › doi › 10

   2 days ago · Description. Many fundamental combinatorial problems, arising in such diverse fields as artificial intelligence, logic, graph theory, and linear algebra, can be formulated as Boolean constraint satisfaction problems (CSP). This book is devoted to the study of the complexity of such problems.

7. prezi.com › p › c-nti8uhtdamUnderstanding Constraint Satisfaction Problems by Iram ...

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   5 days ago · Real-world success stories of CSP implementation include the use of constraint programming in airline crew scheduling to minimize costs and improve workforce satisfaction. Additionally, CSPs have been utilized in sports scheduling algorithms for creating fair and balanced game schedules. • The simplest type is the unary constraint, which.

8. cbriansmith.substack.com › p › flipping-the-scriptFlipping the Script: Unleashing the Power of 'Flipped ...

   cbriansmith.substack.com › p › flipping-the-script

   1 day ago · (EG: LLM can ask customers a series of targeted questions to diagnose their problems, reducing the need for multiple back-and-forth communications.) Strategic Decision-Making: Leaders can gain a comprehensive view of their business landscape. (EG: A CEO uses an AI-driven model to ask questions about emerging market trends, competitive threats ...

9. journals.sagepub.com › doi › 10Mitigation in Marketing: Concept, Definition, and Scope

   journals.sagepub.com › doi › 10

   1 day ago · As an example, previous scholarship on customer satisfaction took a maximization-oriented perspective and focused only on positive outcomes such as maximizing stock returns (Aksoy et al. 2008) and customer loyalty (Mittal and Kamakura 2001). However, by incorporating a MiM perspective, more recent scholarship has examined outcomes of increasing ...