Integrating games into the computer science curriculum has been gaining acceptance in recent years, particularly when used to improve student engagement in introductory courses. This paper argues that games can also be useful in upper level courses, such as general artificial intelligence and machine learning. We provide a case study of using a Mario game in a machine learning class to provide one successful data point where both content-specific and general learning outcomes were successfully achieved.

We introduce a Java graphical gaming framework that enables students in an introductory artificial intelligence (AI) course to immediately apply and visualize the topics from class. We have used this framework in teaching a mixed undergraduate/graduate AI course for six years. We believe that the use of games motivates students. The graphical nature of each game enables students to quickly see how well their algorithm works. Because the topics in an introductory AI course vary widely, students apply their algorithms to multiple game environments. A final challenging environment enables them to tie together the concepts for the entire semester.

The undergraduate computer science curriculum is generally focused on skills and tools;  most students are not exposed to much  research in the field, and do not learn how to navigate the research literature.  We describe how science fiction reviews were used as a gateway to research reviews.  Students learn a little about current or recent research on a topic that stirs their imagination, and learn how to search for, read critically, and compare technical papers on a topic related their chosen science fiction book, movie, or TV show.

RoadNarrows has entered the “desktop-size” 5 degree of freedom robot arm, Hekateros, in the Small-Scale Manipulator Challenge, at the 2011 AAAI conference. Hekateros is utilizing a variety of sensors and software to successfully perceive and manipulate the chess gam

Students have implemented approaches to these problems, using our HRTeam framework (Sklar et al. 2010), which is built on Player/Stage (Vaughan and Gerkey 2007).

We present the initial stage of our research on Learning from Demonstration algorithms. We have implemented an algorithm based on Confident Execution, one of the components of the Confidence-Based Autonomy algorithm developed by Chernova and Veloso. Our preliminary experiments were conducted first in simulation and then using a Sony AIBO ERS-7 robot. So far, our robot has been able to learn crude navigation strategies, despite limited trials. We are currently working on improving our implementation by including additional features that describe more broadly the state of the agent. Our long term goal is to incorporate Learning from Demonstration techniques in our HRTeam (human/multi-robot) framework.

The design and implementation of a robotic chess agent is described. Shallow Blue, a competitor in the AAAI 2011 Small Scale Manipulation Challenge, is constructed with low-cost components including Lego NXT bricks and is programmed using Java and Lejos.

The initial idea of using Lego Mindstorms Robots for student courses had soon to be expanded to a simulation environment as the user base in students grew larger and the need for parallel development and testing arose. An easy to use and easy to set up means of providing positioning data led to the creation of an indoor positioning system so that new users can adapt quickly and successfully, as sensors on the actual robots are difficult to configure and hard to interpret in an environmental context. A global positioning system shared among robots can make local sensors obsolete and still deliver more precise information than currently available sensors, also providing the base necessary for the robots to effectively work on shared tasks as a group. Further more, a simulator for robots programmed with Fujaba and Java which was developed along the way can be used by many developers simultaneously and lets them evaluate their code in a simple way, while close to real-world results.

This paper describes our efforts preparing a mobile manipulator for the 2011 AAAI Small Scale Manipulation Challenge. We describe our approach to building a low-cost mobile manipulator for human-scale environments using ROS and off-the-shelf sensory and servos.

That drone and its basic capabilities are summarized in Figure 1. The flexibility and controllability of quadrotor helicopters have made them a recent focus of interest among robotics and AI research groups. At the same time, their popularity has led to a wide range of commercially available platforms, some at prices accessible for undergraduate educational use. This project evaluates the ARDrone quadrotor helicopter as a basis for use in undergraduate classes such as robotics, computer vision, or embodied AI. We have encountered both successes and frustrations in using the ARDrone to date. Looking forward, the quadrotor's capabilities do seem a promising basis for future curricular offerings.