This article tackles a significant aspect of the problem of scheduling personal individual activities, that is, the generation of qualitative, significantly different alternative plans. Solving this problem is important for intelligent calendar applications, since average users cannot adequately express their preferences over the way their activities should be scheduled in time, thus it is common that they are not satisfied by the plans generated for them by a scheduler, although they are near-optimal according to their stated preferences. Hence generating alternative plans and asking the user to select one among them is a sensible approach, provided that the alternative plans are both near-optimal, according to the user-defined preferences, as well as significantly different to each other, in order to increase the chances that at least one of them satisfies the user. Furthermore, based on the assumption that a user might systematically misweight his preferences over the various aspects of a plan, an online non-intrusive method to learn his actual preferences is presented, based on monitoring his selections over the alternative plans. The proposed methods have been evaluated successfully on a variety of problems. Furthermore, they have been implemented in two deployed systems.
['Anastasios Alexiadis', 'Ioannis Refanidis']
Alternative Plan Generation and Online Preference Learning in Scheduling Individual Activities