5. Methodology

1. http://ecsl.cse.unr.edu 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 6. Results XCS outperforms J48 on the test sets Learning Classifier Systems for User Context Learning Anil Shankar, Sushil J Louis anilk@cse.unr.edusushil@cse.unr.edu Evolutionary Computing Systems Lab (ECSL) Dept. of Computer Science and Engineering, University of Nevada, Reno, USA 1. Motivation • 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 Fig.1 Architecture 2. Objectives Fig.2 Sycophant – User Interface • 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 Fig.3 XCS Architecture 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 3. Our approach 7. Discussions & Conclusions • 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 • 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 Acknowledgments: This work was supported in part by contract number N00014-0301-0104from the Office of Naval Research.