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The algorithm quasi (AQ) is a powerful machine learning methodology aimed at learning symbolic decision rules from a set of examples and counterexamples. It was first proposed in the late 1960s to solve the Boolean function satisfiability problem and further refined over the following decade to solve the general covering problem. In its newest implementations, it is a powerful but yet little explored methodology for symbolic machine learning classification. It has been applied to solve several problems from different domains, including the generation of individuals within an evolutionary computation framework.The book introduces the main concepts of the AQ methodology and describes AQ for zoonotic disease diagnosis, a tailored implementation of the AQ methodology to solve the problem of detecting zoonotic diseases by using a number of parameters and symptoms.