Consul, SakshamStojcheski, JugoslavLieder, FalkMarky, KarolaGrünefeld, UweKosch, Thomas2022-08-302022-08-302022https://dl.gi.de/handle/20.500.12116/39081Many people procrastinate and struggle to prioritize their most important work. To help their users overcome such problems, gamified productivity tools like Habitica use heuristic point systems that can be counterproductive. We recently proposed a more principled way to compute point values that avoids such problems. Although it was promising in theory, it required large amounts of computation even for very short to-do lists. Here, we present a scalable approximate method that makes our principled approach to to-do list gamification useable in the real world. Our method leverages artificial intelligence to generate a gamified to-do lists, where each task is incentivized by a number of points that communicates how valuable it is in the long-run. What makes our new method more scalable is that it decomposes the problem of computing long-term plans for how the user can best achieve their goals into a hierarchy of smaller planning problems. We assessed the scalability of our method by applying it to to-do lists with increasingly larger numbers of goals, sub-goals, and tasks, and we also increased the number of nested levels of the goal hierarchy. We found that the method can enable web and mobile applications to compute excellent point systems for fairly large to-do lists, with up to 576 tasks spread out over up to 9 different top-level goals. Our method freely available through an API1. This makes it easy to use our method in gamified web applications and mobile apps.engamificationproductivity toolsprioritizationAIprocrastinationText/Workshop Paper10.18420/muc2022-mci-ws08-241