1. A relational artificial intelligence system in a digital computer performing automatic knowledge acquisition from a set of data records, generating a set of relational knowledge bases through the aid of a set of CPUs of said computer, and performing inferences on said set of relational knowledge bases to obtain inference results based on a set of required data, comprising: input/output means for acquiring data and generating output; computer storing means for storing data and computer programs; a set of relational inductive engines being a set of executable computer programs stored in said computer storing means for automatically discovering knowledge from said set of data records and generating said set of relational knowledge bases through the aid of said set of CPUs of said computer, each one of said relational knowledge bases comprising a set of knowledge relations; and a set of relational inference engines being a set of executable computer programs stored in said computer storing means for reasoning about said set of relational knowledge bases and obtaining stud inference results based on said set of required data means for storing all permissible values in fields of each attribute of said decision relations in said computer storing means; means for assigning a code to each one of said permissible values; means for translating said permissible values to code; means for creating a set of code decision relations; and means for translating said code to said permissible values; wherein A. said set of relational inductive engines comprising: a. means for acquiring data from said set of data records through said input/output means; b. means for selecting a set of value-attributes and a set of decision-attributes from all attributes of said set of data records; c. means for creating a set of decision relations in said computer storing means, each one of said decision relations comprising said set of value-attributes, said set of decision-attributes, and more than one record selected from said set of data records; d. means for clustering tuples in each one of said set of decision relations into positive instances and negative instances; e. means for counting positive counts of said positive instances and negative counts of said negative instances in each one of said set of decision relations; f. means for conjunctive generalization comprising means for generalizing values in value-fields in each of said set of decision relations; and g. means for generating said set of relational knowledge bases in said computer storing means, each of said relational knowledge bases comprising a set of knowledge relations; B. said set of relational inference engines comprising: a. means for scanning all value-fields in each one of said set of knowledge relations of said set of relational knowledge bases generated by said set of relational inductive engines in said computer storing means; b. means for accepting said required data for said value-attributes; c. means for comparing said required data with values in said value-fields and performing true-false tests for said value-fields; d. means for tuple testing, for performing true-false tests of status factors of tuples of each one of said knowledge relations; e. means for determining a set of tuples with true status factors; and f. means for executing decision statements in said set of tuples through said input/output means.