FINDING A MAXIMALLY SPECIFIC HYPOTHESIS In order to search the best toit hpoes, there are two algorithms are in my in is recommended. One is the findest algorithm. So, finders algorithm is finding a maximally specific hypothesis. So, the algorithm states we have to initialize H to the most specific hypothesis, that means all five values. For each positive training instance X, we have to take up the given training examples one by one. For each attribute constraint, for every attribute in that in example X, we have to take the constraint the value, and if the constraint is satisfied by X, then do nothing. Suppose in our most specific hypothesis, the values of the instance is satisfying, we need not to do anything. Otherwise, replace the most specific hypothesis value with the value available in the instance. That is what the algorithm says. So, this step is repeated for all examples given. Then, finally, the sh the hypothesis, what is generalized, will be the one which is output. So, we have here, we begin with the most specific hypothesis, then generalizing it to the more general form. That is what so illustration.