Though attention to evaluating human-robot interfaces has increased in recent years, there are relatively few reports of using evaluation tools during the development of humanrobot interaction (HRI) systems to improve their designs. Heuristic evaluation is a technique suitable for such applications that has become popular in the humancomputer interaction (HCI) community. However, it requires usability heuristics applicable to the system environment. This work contributes a set of heuristics appropriate for use with HRI systems, derived from a variety of sources both in and out of the HRI field. Evaluators have successfully used the heuristics on an HRI system, demonstrating their utility against standard measures of heuristic effectiveness.
Microsoft researchers have created an artificial intelligence-based system that learned how to get the maximum score on the addictive 1980s video game Ms. Pac-Man, using a divide-and-conquer method that could have broad implications for teaching AI agents to do complex tasks that augment human capabilities. The team from Maluuba, a Canadian deep learning startup acquired by Microsoft earlier this year, used a branch of AI called reinforcement learning to play the Atari 2600 version of Ms. Pac-Man perfectly. Using that method, the team achieved the maximum score possible of 999,990. Doina Precup, an associate professor of computer science at McGill University in Montreal said that's a significant achievement among AI researchers, who have been using various videogames to test their systems but have found Ms. Pac-Man among the most difficult to crack. But Precup said she was impressed not just with what the researchers achieved but with how they achieved it.
An award-winning effort at CERN has demonstrated potential to significantly change how the physics based modeling and simulation communities view machine learning. The CERN team demonstrated that AI-based models have the potential to act as orders-of-magnitude-faster replacements for computationally expensive tasks in simulation, while maintaining a remarkable level of accuracy. Dr. Federico Carminati (Project Coordinator, CERN) points out, "This work demonstrates the potential of'black box' machine-learning models in physics-based simulations." A poster describing this work was awarded the prize for best poster in the category'programming models and systems software' at ISC'18. This recognizes the importance of the work, which was carried out by Dr. Federico Carminati, Gul Rukh Khattak, and Dr. Sofia Vallecorsa at CERN, as well as Jean-Roch Vlimant at Caltech.
Computational Thinking (CT) is considered a core competency in problem formulation and problem solving. We have developed the Computational Thinking using Simulation and Modeling (CTSiM) learning environment to help middle school students learn science and CT concepts simultaneously. In this paper, we present an approach that leverages multiple linked representations to help students learn by constructing and analyzing computational models of science topics. Results from a recent study show that students successfully use the linked representations to become better modelers and learners.