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Flashcards

04 Constraint Satisfaction Problems

Timo Bierwirth Artificial Intelligence Alemão 11 flashcards

Flashcards do conjunto (11)

  1. 1What are the differences between CSPs and standard search problems? What is the advantage here?-Factored representations of each state (variables with values)
    -Goal test: value for each variable that satisfies all constraints of the problem
    -Path is irrelevant, every solution appears at depth n with n variables (use depth-first)
    Benefit: general-purpose algorithms by exploiting internal structure of the states
  2. 2How does the standard search formulation for all CSPs (Naive Search Approach) look like?-Initial state: empty assignment
    -Successor function: assigns value to unassigned variable not conflicting with current assignment
    -Goal test: current assignment is complete
  3. 3When can Backtracking be applied and how does the method look like?For CSPs with single-variable assignments (commutative)
    1.Which variable should be assigned next: first degree heuristic (largest number of constraints), then minimum-remaining-values (MRV) heuristic (fail-first)
    2.In what order should values be tried: least constraining value (fail-last)
  4. 4What is inference and which two techniques are there?Act or process of deriving logical conclusions from known premises
    -Forward Checking
    -Arc Consistency
  5. 5How does Forward Checking work?Consistency (between variable X and variables Y) check eliminations of domains improve the search
  6. 6How does Arc Consistency work?If for every value in the one reduced domain there exists a value in the other, which satisfies binary constraint
    -Procedure: check consistency, remove if inconsistent value, check with other neighbors
  7. 7How can tree-structured CSPs be solved?1.Topological sort: choose variable as a root & order from root to leave
    2.Make obtained variable order direct arc-consistent
    3.March down list of variables & choose any remaining value (no backtracking required!)
  8. 8What are two approaches for nearly tree-structured CSPs?-Conditioning
    -Tree decomposition
  9. 9How does Conditioning work?1.Choose subset - graph becomes tree after removal of S
    2.Remove values from other domains if assignment is constraint-satisfying to variables in S - if remaining CSP has solution, return it with the one of S
  10. 10How does Tree Decomposition work?1.Set of connected subproblems connected by a super-tree
    2.Solve each subproblem independently
    3.If every subproblem is solved, attempt of global solution using efficient algorithm for trees
  11. 11What must be satisfied for the application of Tree Decomposition?-every part of the original problem must appear in at least one subproblem
    -connected parts of the orginial problem must be connected in at least one subproblem
    -if variable appears in two subproblems, it must appear in every subproblem along this path
    -if any subproblem has no solution, the entire problem has no solution

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Artificial Intelligence FlashCards sobre 04 Constraint Satisfaction Problems, criado por Timo Bierwirth em 22-02-2018.
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Timo Bierwirth
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