Abstract: Plan recognition algorithms need to maintain all candidate hypotheses which are consistent with the observations, even though there is only a single hypothesis that is the correct one. Unfortunately, the number of possible hypotheses can be exponentially large in Continue reading
Abstract: Plan Recognition algorithms require to recognize a complete hierarchy explaining the agent’s actions and goals. While the output of such algorithms is informative to the recognizer, the cost of its calculation is high in run-time, space, and completeness. Moreover, Continue reading
Abstract: Volunteer-based crowdsourcing depend critically on maintaining the engagement of participants. We explore a methodology for extending engagement in citizen science by combining machine learning with intervention design. We first present a platform for using real-time predictions about forthcoming disengagement Continue reading
Abstract: Vehicle-to-grid (V2G) is a promising approach whereby electric vehicles (EVs) are used to store excess electricity supply (e.g., from renewable sources), which is sold back to the grid in times of scarcity. In this paper we consider the setting Continue reading