A New Negotiation Mechanism for Agent-Based Lecture Allocation Systems

Elective subject is one of important issues as education program in University. Students can declare their preferences directly by selecting it. In most of university, to allocate elective subjects to the students, university staffs poll students the lectures they want to take. However, due to the limitation of time and number of staffs, the hearing investigation includes the reason and the intention in which students select the lectures. Some students sometimes take a lecture for their career, for academical interest, and for assimilation of knowledge. However, some students might take the lecture following the crowd and take the lecture as Mickey Mouse. The latter case is undesirable for the higher education. To solve the problem, in this paper, we propose a new multi-step lecture allocation method based on students preferences and university intentions. Our protocol has three steps. (1) University warns students who must take a certain lecture to graduate and get a license. (2) Using students preferences, lectures are allocated. (3) Students exchange lectures to increase their intentions and utilities. Our protocol realizes the high performance of allocation compared with brute force algorithm and reducing computational costs compared with optimizations.