Optimizing agent-based meeting scheduling through preference estimation

Meeting scheduling is a routine task that needs to be performed quite regularly and frequently within any organization. Unfortunately, this task can be quite tedious and time-consuming, potentially requiring a several rounds of negotiations among many people on the meeting date, time and place before a meeting can finally be confirmed. The objective of our research is to create an agent-based environment within which meeting scheduling can be performed and optimized. For meeting scheduling, we define optimality as the solution that has the highest average preference level among all the possible choices. Our model tries to mimic real life in that an individual's preferences are not made public. Without complete information, traditional optimal algorithms, such as A* will not work. In this paper, we present a novel “preference estimation” technique that allows us to find optimal solutions to negotiations problems without needing to know the exact preference models of all the meeting participants beforehand. Instead, their preferences are “estimated” and built on the fly based on observations of their responses during negotiation. Another unique contribution is the use of “preference rules” that allow preferences to change dynamical as scheduling decisions are made. This mimics changing preferences as schedule gets filled. This paper uses two negotiation algorithms to compare the effect of “preference estimation”—one that is based on negotiation through relaxation and the other that extends this with preference estimations. Simulations were then performed to compare these algorithms.