1. 5. Methodology Compare the performance of XCS with an implementation of C4.5, a decision tree algorithm called J48, on the reminder generation task. Exemplar from our context data-set: Sensor value grouping : Sensor-Count, Sensor-All5, Sensor-Any5, Sensor-All1, Sensor-Any1 and Sensor-Immediate. User1 – user name 05.00 – appointment time 0, 0, 0, 0, 0, 0 – motion-sensor values 7, 0, 1, 0, 0, 0 – speech-sensor values 20, 1, 1, 1, 1, 1 – process-1 sensor values 20,1, 1, 1, 1, 1 - process-2 sensor values 20, 1, 1, 1, 1, 1 - process-3 sensor values 0, 0, 0, 0, 0, 0 - process-4 sensor values 20, 1, 1, 1, 1, 1 - process-5 sensor values 0, 0, 0, 0, 0, 0 – keyboard sensor values 0, 0, 0, 0, 0, 0 – mouse sensor values 3 – reminder type preferred by a user The same exemplar in a bit string representation for XCS 10001010101010101001110010110000 10010000100110111101001101111010 01111111000000000000100111111100 00000000000000000000:3 7. Discussions & Conclusions 2. Objectives Apply machine learning techniques to data gathered from simple context sensors to build improved human computer interfaces Use simple sensors to continuously gather data on a computer system’s internal and external environment Mine this context data for useful user-behavior patterns to better predict user preferences (behavior) and improve user interaction Apply XCS, a Genetics Based Machine Learning Technique to learn a mapping from user- related contextual features to reminder types 3. Our approach Integrates approaches in context aware systems and data mining Considers a computer as a stationary robot with simple sensors for sensing the external and internal environments Builds user-interfaces on this stationary model of a computer 4. Sycophant A simple calendaring application program that stores appointments and reminds a user using different reminder types Generates four types of reminders : visual (a pop-up window), speech (using a text-to-speech system), both, or neither Continuously gathers binary activity data from the keyboard, mouse, a motion detector, and a speech sensor; monitors the activity of five processes on the computer Generates a reminder and expects the user to indicate whether Sycophant used the correct reminder type Combine expert-generated rules with machine learned rules and use this combined knowledge to design better adaptive user interfaces Collect data from different users to scale up our research in the area of context learning applications and consider the possibility for personalization to individual users Fig.1 Architecture Fig.2 Sycophant – User Interface Fig.3 XCS Architecture Anil Shankar, Sushil J Louis anilk@cse.unr.eduanilk@cse.unr.edu sushil@cse.unr.edusushil@cse.unr.edu Evolutionary Computing Systems Lab (ECSL) Dept. of Computer Science and Engineering, University of Nevada, Reno, USA http://ecsl.cse.unr.edu Acknowledgments: This work was supported in part by contract number N00014-0301-0104 from the Office of Naval Research. 6. Results XCS outperforms J48 on the test sets Current computer applications pay insufficient attention to a computer’s environment. These application programs lack context-awareness, and therefore they can only make weak attempts to adapt to individual user needs. Our approach is to: use simple sensors to collect data on a computer system’s internal and external environment. mine this contextual information for useful user-behavior patterns to better predict user preferences (behavior) and improve user interaction To substantiate our hypothesis, we have designed Sycophant, a context learning calendaring application program that learns a mapping from user-related contextual features to reminder actions 1. Motivation