1. Efficient Interaction Strategies for Adaptive Reminding Julie S. Weber & Martha E. Pollack Adaptive Reminder Generation SignalingIntended Approach Learning Computer Science and Engineering University of Michigan, Ann Arbor, MI, U.S.A. Justifications A single, sparse reminder may not be enough to convince that user to perform the task immediately. With a justification, the user is more likely to comply: Reminder Granularity Techniques taken from the area of machine learning can effectively enhance our solutions to the challenges described above: the system can learn how best to interact with a particular user based on that user's pattern of compliance with the reminders received. [5:30pm] Knight Rider is on at 6:00, so there is just enough time for exercise beforehand. [2:00pm] Reminder; your paper deadline is at 4:00, and your 3:00 meeting lasts 2 hours. [8:30am] Time to prepare breakfast. [8:30am] Remove eggs from refrigerator. [8:31am] Prepare frying pan on stove.... Meeting with CEO in 10 minutes OK Lunch meeting in cafe at noon 11:35am Time for your walk with Rhonda References [1] Mark, B., and Perrault, R. C. CALO: Cognitive Assistant that Learns and Organizes. http://www.ai.sri.com/project/CALO [2] Pollack, M. E., Brown, L., Colbry, D., McCarthy, C. E. Orosz, C., Peintner, B., Ramakrishnan, S., and Tsamardinos, I. Autominder: An Intelligent Cognitive Orthotic System for People with Memory Impairment. Robotics and Autonomous Systems (44) 273-282, 2003. [3] Rudary, M., Singh, S., and Pollack, M. E. Adaptive Cognitive Orhthotics: Combining Reinforcement Learning and Constraint-Based Temporal Reasoning. International Conference on Machine Learning 2004. [4] Weber, J. S., and Pollack, M. E. Entropy- Driven Online Active Learning for Interactive Calendar Management. International Conference on Intelligent User Interfaces 2007. Creating a system that builds upon the initial efforts reported in [3] and [4] requires: Adding additional features to the action space of an adaptive reminding system, such that it learns to decide not only when to issue a particular reminder, but also how the reminder should be issued. Deciding when it is appropriate to perform active learning of user preferences over the features of a reminder. A new reminder plan is generated at the start of each day, based on the user’s current schedule and to-do items. As new activities and tasks are scheduled and depending on the user’s current task, this plan is updated to reflect those new tasks requiring reminders. When a user performs or fails to perform the task or activity associated with a particular reminder, the system can update its policy for issuing reminders accordingly. Summary There are a number of dimensions to intelligent reminding that must be explored to create a personalized, adaptive system. Two learning techniques that we propose to explore in this context are reinforcement learning and supervised learning directed by an active learning component. Must track level of impairment Different users may respond more readily to, or have different preferences for, certain types of reminders university of michigan artificial intelligence laboratory Features: - Justifications - Adaptive Reminder Granularity - Adaptive Signaling Testbeds: - Assistive Technology for People with Cognitive Impairment (e.g., Autominder [2]) - Smart Office Assistants (e.g., CALO [1]) Adaptation Techniques: - Supervised Learning, with an active learning component - Reinforcement Learning Reinforcement learning to determine when certain reminders should be issued [3]. Supervised learning of scheduling preferences by way of active learning [4]. Architecture Reminders Calendar Updates Adaptive Reminding System Reminder Plan To Do List Location Info Task Info