A First-Order Logic Framework of Major Choosing Decision Making With an Uncertain Reasoning Function

This paper proposes an auto-decision making system, knowledge-based major choosing decision (KMCD), which helps remote students choose their majors. It is built within a first-order reasoning system with an uncertain reasoning function. The knowledge of both students who want to choose their majors and who have finished their studies are used in the decision making process of the system. KMCD provides personalized suggestions to those students who want to choose majors. Both the learning abilities of students and the career prospects of majors are carefully analyzed on the basis of the knowledge in database. As these analyzing results are well utilized in the decision making process of KMCD, it can be expected that the students who take the suggestions will have a better chance to get graduated smoothly and start their careers successfully after graduation. A detailed description of KMCD is given, which shows that the system is actually a reasonable decision making framework with a capability of expansion of other knowledge which is supposed to be useful for major choosing. An experimental study on data of real college students is devised to justify the effectiveness of KMCD.