REU 2004 Computer Science and Engineering Department The University of Texas at Arlington Research Experiences for Undergraduates in Distributed Rational Agents

REU 2004 Distributed Rational Agents • Research projects will generally involve small groups of students (1 - 3) working with graduate students and a faculty advisor. • Research Areas: – Intelligent Device Control – Connected Devices – Home Simulation

REU 2004 Distributed Rational Agents Goals: • Learn research methodologies • Perform research in the context of an on-going project • Develop agent technologies Course Requirements: • Classroom sessions will cover basic materials • Every student will present her/his research results • Every student will write a report of the work • At the end of the program all results will be presented in an "open-house" workshop

Computer Science and Engineering Department The University of Texas at Arlington MavHome: An Intelligent Home Environment

Motivations •Unified project incorporating varied AI techniques, cross disciplinary with mobile computing, databases, multimedia, and others •High visibility •Possible commercial implications

Smart House Face recognition, automated door entry Smart sprinklers Lighting control Door/lock controllers, Surveillance system Robot vacuum cleaner Robot lawnmower Intelligent appliances Climate control Intelligent Entertainment Automated blinds Remote site monitoring and control Assistance for disabilities

UTA MavHome Capabilities • UTA Project Unique –Focus on entire home • House perceives and acts –Sensors –Controllers for devices –Connections to the mobile user and Internet • House optimizes goal function –Maximize inhabitant comfort –Minimize cost –Maximize user productivity –Maximize security

Smart Home - An Adaptive Environment • Smart Home is a home environment that adapts to the inhabitants • It has to sense the state of the home and the presence of people • It has to predict their behavior • It has to make decisions in order to automate the home

MavHome Architecture Machine Learning

UTA MavHome Components • Decision Layer – Hierarchical Reinforcement Learning • Information Layer – Reactive / Proactive Information Repository – Predicting inhabitant and house behaviors – Mobility prediction • Communication Layer – Intelligent routing – Supporting location-aware / context-aware services • Specialized Agents – Smart distributed sensor network – Personal service robots – Multimedia agent

REU Summer Projects •Interfaces for Automatic Health Monitoring •Acquisition of TV Viewing Preferences from Closed Captioning •Interface and Visualization of Home Performance Measures •Interfaces and Control of Virtual Appliances •Optimized Human Interfaces for Intelligent Environments •Anomaly Detection and Identification in Smart Homes •Voice over IP through Robotic Assistants •3-D Distributed Computer Modeling for Home Simulation

REU Summer Projects •Integrated Automatic Entry System using Face, Voice, and Fingerprint Recognition •In-Door Localization Using Wireless Signals •Robot Vision and Locomotion for AIBO dog •Mobile Robot Control

Additional Information •Detailed MavHome description: •REU class materials: