Proceedings Abstracts of the Twenty-Fifth International Joint Conference on Artificial Intelligence

Interactive Scheduling of Appliance Usage in the Home / 869
Ngoc Cuong Truong, Tim Baarslag, Sarvapali D. Ramchurn, Long Tran-Thanh

We address the problem of recommending an appliance usage schedule to the homeowner which balances between maximizing total savings and maintaining sufficient user convenience. An important challenge within this problem is how to elicit the the user preferences with low intrusiveness, in order to identify new schedules with high cost savings, that still lies within the user's comfort zone. To tackle this problem we propose iDR, an interactive system for generating personalized appliance usage scheduling recommendations that maximize savings and convenience with minimal intrusiveness. In particular, our system learns when to stop interacting with the user during the preference elicitation process, in order to keep the bother cost (e.g., the amount of time the user spends, or the cognitive cost of interacting) minimal. We demonstrate through extensive empirical evaluation on real-world data that our approach improves savings by up to 35%, while maintaining a significantly lower bother cost, compared to state-of-the-art benchmarks.