Azaria, Amos (Carnegie Mellon University) | Rosenfeld, Ariel (Bar-Ilan University) | Kraus, Sarit (Bar-Ilan University) | Goldman, Claudia V. (Advanced Technical Center, General Motors Israel) | Tsimhoni, Omer (General Motors Warren Technical Center)
Reducing energy consumption of climate control systems is important in order to reduce human environmental footprint. Our approach takes into account both the energy consumption of the climate control system and the expected comfort level of the driver. We therefore build two models, one for assessing the energy consumption of the climate control system as a function of the system's settings, and the other, models human comfort level as a function of the climate control system's settings. Using these models, the agent provides advice to the driver considering how to set the climate control system.
Rosenfeld, Avi (Jerusalem College of Technology) | Bareket, Zevi (University of Michigan) | Goldman, Claudia V. (General Motors) | Kraus, Sarit (Bar-Ilan University) | LeBlanc, David J. (University of Michigan) | Tsimhoni, Omer (General Motors)
Such interactive activity leads us to consider intelligent and advanced ways of interaction leading to cars that can adapt to their drivers.In this paper, we focus on the Adaptive Cruise Control (ACC) technology that allows a vehicle to automatically adjust its speed to maintain a preset distance from the vehicle in front of it based on the driver's preferences. We introduce a method to combine machine learning algorithms with demographic information and expert advice into existing automated assistive systems. This method can reduce the interactions between drivers and automated systems by adjusting parameters relevant to the operation of these systems based on their specific drivers and context of drive. While generic packages such as Weka were successful in learning drivers' behavior, we found that improved learning models could be developed by adding information on drivers' demographics and a previously developed model about different driver types.
The 2005 Autonomous Agents and Multiagent Systems Conference (AAMAS 2005) was held July 25-29, 2005, at the University of Utrecht, the Netherlands. This report reviews the activities of that conference, including the workshop and tutorial programs, the main conference and poster tracks, the industry paper track, the demonstration track and sponsor demonstration sessions, the invited talks, exhibition, doctoral mentoring program, as well the sponsorship and scholarships activities.