Perpetual voting considers sequences of decis ions made by the same electorate, where fairness must be evaluated over time rather than perdecision [16]. A centralchallenge in this setting is ensuring adequaterepresentation for voters who are repeatedly in the minority. Traditional a ggregation rules, such as majority voting or Borda count, fail in this regard: they offer no guarantees of long-term fai rness or cumulative influence. In response, methods such as Perpetual Phragmén [17] and Perpetual Consensus [16] hav e been proposed to distribute influence more equitably over time. However, they rely on full knowledge of all voters ' approval sets, implicitly requiring consistent voter participation, a condition which can be hard to satisfy in real-world contexts. Real-world elections face various practical constraints-- including scheduling conflicts, limited resources, and restricted information access--that inevitably prevent vote rs from participating consistently.

The latest issue of the IEEE Robotics & Automation Magazine features a special report on open-source robotics hardware and its impact in the field. We've seen how, over the last several years, open source software--platforms like the Robot Operating System (ROS), Gazebo, and OpenCV, among others--has played a huge role in helping researchers and companies build robots better and faster. Can the same thing happen with robot hardware? It's already happening, says robotics researcher and RAM editor-in-chief Bram Vanderborght, who explains that building hardware has gotten much easier thanks to things like 3D printers, laser cutters, modular open electronics kits, and other rapid prototyping and fabrication techniques. And while "open-source robotics hardware is taking longer to catch on" compared to open-source robotics software, he notes that "several impressive examples exist, taking advantage of benefits of those novel rapid prototyping possibilities."

The candidate will join the AI lab (ai.vub.ac.be) of the Department of Computer Science. The candidate is expected to contribute to the research and teaching of the AI team. The lab has strong national as well as international collaborations and based in the heart of Brussels, offers plenty of possibilities for collaboration with industry. Founded in 1983, by Luc Steels, the AI lab became the first Artificial Intelligence lab on European mainland. The lab is active in a variety of AI domains, including Evolution of language, machine learning, multi-agent systems, reinforcement learning, evolutionary systems and bioinformatics.

List of chairs of the 2015 IJCAI conference programs.