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Examples of problems that can be modeled as a constraint satisfaction problem include: Type inference [3] [4] Eight queens puzzle; Map coloring problem; Maximum cut problem [5] Sudoku, crosswords, futoshiki, Kakuro (Cross Sums), Numbrix/Hidato and many other logic puzzles; These are often provided with tutorials of CP, ASP, Boolean SAT and SMT ...
Toy problems were invented with the aim to program an AI which can solve it. The blocks world domain is an example for a toy problem. Its major advantage over more realistic AI applications is, that many algorithms and software programs are available which can handle the situation. [2] This allows to compare different theories against each other.
The Weiler–Atherton algorithm overcomes this by returning a set of divided polygons, but is more complex and computationally more expensive, so Sutherland–Hodgman is used for many rendering applications. Sutherland–Hodgman can also be extended into 3D space by clipping the polygon paths based on the boundaries of planes defined by the ...
Algorithm Affine-Scaling . Since the actual algorithm is rather complicated, researchers looked for a more intuitive version of it, and in 1985 developed affine scaling, a version of Karmarkar's algorithm that uses affine transformations where Karmarkar used projective ones, only to realize four years later that they had rediscovered an algorithm published by Soviet mathematician I. I. Dikin ...
The problem does have a variant which is more tractable. Given any positive integer k≥3, the k-set packing problem is a variant of set packing in which each set contains at most k elements. When k=1, the problem is trivial. When k=2, the problem is equivalent to finding a maximum cardinality matching, which can be solved in polynomial time.
For these reasons, it is often non-trivial to group the extracted edge features to an appropriate set of lines, circles or ellipses. The purpose of the Hough transform is to address this problem by making it possible to perform groupings of edge points into object candidates by performing an explicit voting procedure over a set of parameterized ...
The Positive predictive value (PPV) of a test is the proportion of persons who are actually positive out of all those testing positive, and can be calculated from a sample as: PPV = True positive / Tested positive. If sensitivity, specificity, and prevalence are known, PPV can be calculated using Bayes theorem.
Apriori [1] is an algorithm for frequent item set mining and association rule learning over relational databases.It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.