A Robot-based Laboratory for Teaching Artificial Intelligence

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There is a growing consensus among computer science faculty that it is quite difficult to teach the introductory course on Artificial Intelligence (AI) well. This is because AI lacks a unified methodology, overlaps with many other disciplines, and involves a wide range of skills from very applied to quite formal. This project addresses these problems by (1) offering a unifying theme that draws together the disparate topics of AI; (2) focusing the course syllabus on the role AI plays in the core computer science curriculum; and (3) motivating the students to learn by using concrete, hands-on laboratory exercises. The main theme is to conceive of each topic in AI (such as search, planning, learning, vision) as a robotics task and then to have the students build their own robots and program them to accomplish the tasks. By constructing a physical entity in conjunction with the code to control it, the students have a unique opportunity to tackle many central issues of computer science directly including the interaction between hardware and software, space complexity in terms of the memory limitations of the robot's controller, and time complexity in terms of the speed of the robot's action decisions. More importantly, the robot theme provides a strong incentive toward learning because students want to see their inventions succeed. The goal of this project is to equip two identical robotics laboratories for teaching AI. Each laboratory contains a collection of robot building stations as well as one sophisticated off-the-shelf robot to demonstrate more advanced topics to the students. The deliverable for this project is a laboratory manual that is closely integrated with a semester-long AI course syllabus. The manual is being developed collaboratively and tested separately at the participating institutions. The overall effectiveness of this project will be determined by student feedback and performance. The project results can be disseminated through a variety of channels: a SIGART column on AI Education, special conference tracks on AI Education, summer training workshops for AI educators, and World Wide Web repositories on AI. This project offers a remedy for the difficulties facing AI educators by offering a cohesive framework for the presentation of the material that emphasizes AI's relationship with computer science and motivates the students to learn.