In this paper, we report on the efforts at the University of Southern California to teach computer science and artificial intelligence with games because games motivate students, which we believe increases enrollment and retention and helps us to educate better computer scientists. The Department of Computer Science is now in its second year of operating its Bachelor's Program in Computer Science (Games), which provides students with all the necessary computer science knowledge and skills for working anywhere in industry or pursuing advanced degrees but also enables them to be immediately productive in the game development industry. It consists of regular computer science classes, game engineering classes, game design classes, game crossdisciplinary classes and a final game project. The Introduction to Artificial Intelligence class is a regular computer science class that is part of the curriculum. We are now converting the class to use games as a motivating topic in lectures and as the domain for projects. We describe both the new bachelor's program and some of our current efforts to teach the Introduction to Artificial Intelligence class with games.
Faculty at liberal arts colleges are often challenged to offer a quality education to their students, complete with opportunities for undergraduate research. To guard against a curriculum that is too theoretical, students want to see applications of their course work and tangible results of their efforts. Like all computer science educators, we want to attract students to our discipline. The use of robotics can often be part of the answer in each of these realms.
Robotics projects coupled with agent-oriented trends in artificial intelligence education have the potential to make introductory AI courses at liberal arts schools the gateway for a large new generation of AI practitioners. However, this vision's achievement requires programming libraries and low-cost platforms that are readily accessible to undergraduates and easily maintainable by instructors at sites with few dedicated resources. This article presents and evaluates one contribution toward implementing this vision: the RCXLisp library. The library was designed to support programming of the Lego Mindstorms platform in AI courses with the goal of using introductory robotics to motivate undergraduates' understanding of AI concepts within the agent-design paradigm. The library's evaluation reflects four years of student feedback on its use in a liberal-arts AI course whose audience covers a wide variety of majors. To help establish a context for judging RCXLisp's effectiveness this article also provides a sketch of the Mindstormsbased laboratory in which the library is used.
While there has been an explosion of impressive, data-driven AI applications in recent years, machines still largely lack a deeper understanding of the world to answer questions that go beyond information explicitly stated in text, and to explain and discuss those answers. To reach this next generation of AI applications, it is imperative to make faster progress in areas of knowledge, modeling, reasoning, and language. Standardized tests have often been proposed as a driver for such progress, with good reason: Many of the questions require sophisticated understanding of both language and the world, pushing the boundaries of AI, while other questions are easier, supporting incremental progress. In Project Aristo at the Allen Institute for AI, we are working on a specific version of this challenge, namely having the computer pass Elementary School Science and Math exams. Even at this level there is a rich variety of problems and question types, the most difficult requiring significant progress in AI. Here we propose this task as a challenge problem for the community, and are providing supporting datasets. Solutions to many of these problems would have a major impact on the field so we encourage you: Take the Aristo Challenge!