Baarslag, Tim (University of Southampton) | Aydoğan, Reyhan (Delft University of Technology) | Hindriks, Koen V. (Delft University of Technology) | Fujita, Katsuhide (Tokyo University of Agriculture and Technology) | Ito, Takayuki (Nagoya Institute of Technology) | Jonker, Catholijn M. (Delft University of Technology)
The Automated Negotiating Agents Competition is an international event that, since 2010, has contributed to the evaluation and development of new techniques and benchmarks for improving the state-of-the-art in automated multi-issue negotiation. A key objective of the competition has been to analyze and search the design space of negotiating agents for agents that are able to operate effectively across a variety of domains. The competition is a valuable tool for studying important aspects of negotiation including profiles and domains, opponent learning, strategies, bilateral and multilateral protocols. Two of the challenges that remain are: How to develop argumentation-based negotiation agents that next to bids, can inform and argue to obtain an acceptable agreement for both parties, and how to create agents that can negotiate in a human fashion.
Traditionally, the workshop for the AAAI Robot Competition and Exhibition is held the last day of the NCAI conference, after the robot competition events and exhibitions have conduded. This allows the participants to discuss their actual entries in the robot events, and to talk abouthe results and lessons learned. The events of the Ninth AAAI Robot Competition and exhibition, held July 30 - August 3, 2000, included the popular "Hors d'oeuvres Anyone?" and Challenge competition events, and a new competition event, "Urban Search and Rescue." The exhibition induded groups that wanted to demonstrate work outside of the robot competitions. Students and faculty from University of Arkansas, Northwestern University, Universite de Sherbrooke, Swarthmore College, University of South Florida, and Kansas State University presented research related to contest events.
The Super Bowl is almost here, and that means the NFL has picked the winners of its partly tech-focused 1st and Future competition. The main $50,000 prize for its Innovations to Advance Health and Safety competition is TopSpin's namesake TopSpin360, a helmet-based training device that helps reduce concussions (a serious problem for the NFL as of late) by increasing neck strength. All you have to do is spin your head -- the rotating weight on top generates centripetal force you counteract with your neck muscles. It's also Bluetooth-connected to help guide your training sessions. The $20,000 runner-up, Solius, aims to speed healing by subjecting athletes to narrow spectrums of light that foster the growth of hormones and peptides.
Data analysis education plays an important role in accelerating the efficient use of data analysis technologies in various domains. Not only the knowledge of statistics and machine learning, but also practical skills of deploying machine learning and data analysis techniques, are required for conducting data analysis projects in the real world. Data analysis competitions, such as Kaggle, have been considered as an efficient system for learning such skills by addressing real data analysis problems. However, current data analysis competitions are not designed for educational purposes and it is not well studied how data analysis competition platforms should be designed for enhancing educational effectiveness. To answer this research question, we built, and subsequently operated an educational data analysis competition platform called University of Big Data for several years. In this paper, we present our approaches for supporting and motivating learners and the results of our case studies. We found that providing a tutorial article is beneficial for encouraging active participation of learners, and a leaderboard system allowing an unlimited number of submissions can motivate the efforts of learners. We further discuss future directions of educational data analysis competitions.