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Artificial Intelligence




                    Notes
                                          Example: It learned the concepts House, Tent and Arch.

                                      A near miss is an object that is not a case of the concept in query but that is very alike to
                                       such instances.

                                   Basic Approach of Winston’s Program

                                   1.  Start with a structural description of one recognized instance  of the  concept. Call that
                                       description the concept definition.

                                   2.  Inspect descriptions of other recognized instances of the concepts. Generalize the definition
                                       to include them.
                                   3.  Inspect the descriptions of near misses of the concept. Restrict the definition to exclude
                                       these.

                                   Version Spaces

                                      The objective of version spaces is to generate a description that is reliable with all positive
                                       examples but no negative examples in the training set.

                                      This is another strategy to concept learning.
                                      Version spaces function by sustaining a set of possible descriptions and evolving that set
                                       as new examples and near misses are displayed.

                                      The version space is just a set of descriptions, so an early idea is to maintain an explicit list
                                       of those descriptions.

                                      Version space includes two subsets of the concept space.
                                      One subset known as  G includes most general descriptions reliable with the training
                                       examples  The other  subset includes the most particular descriptions reliable with the
                                       training examples.
                                      The algorithm for narrowing the version space is known as the Candidate elimination
                                       algorithm.

                                   Algorithm: Candidate Elimination


                                      Given: A demonstration language and a set of positive and negative examples articulated
                                       in that language.
                                      Compute: A concept description that is reliable with all the positive examples and none of
                                       the negative examples.
                                   1.  Initialize G to enclose one element.
                                   2.  Initialize S to enclose one element: the first positive element.
                                   3.  Accept new training example. If it is a positive example, first remove from G any descriptions
                                       that do not cover the example. Then modernize the set S to enclose most particular set of
                                       descriptions in the version space that cover the example and the current elements of the
                                       S set. Inverse actions for negative example.
                                   4.  If S and G are both singleton sets, then if they are indistinguishable, output their values
                                       and halt.





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