Abstract
There is a growing consensus among computer science faculty that it is quite difficult to teach the introductory course on Artificial Intelligence well [4, 6]. In part 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. In the funded project described here we have addressed these problems by
" Offering a unifying theme that draws together the disparate topics of AI;
" Focusing the course syllabus on the role AI plays in the core computer science curriculum; and
" Motivating the students to learn by using concrete, hands-on laboratory exercises.
Our approach is to conceive of topics in AI as robotics tasks. In the laboratory, 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, students have a unique opportunity to directly tackle many central issues of computer science 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 towards learning because students want to see their inventions succeed.
This robot-centered approach is an extension of the agent-centered approach adopted by Russell and Norvig in their recent text book [11]. Taking the agent perspective, the problem of AI is seen as describing and building agents that receive perceptions as input and then output appropriate actions based on them. As a result the study of AI centers around how best to implement this mapping from perceptions to actions. The robot perspective takes this approach one step further; rather than studying software agents in a simulated environment, we embed physical agents in the real world. This adds a dimension of complexity as well as excitement to the AI course. The complexity has to do with additional demands of learning robot building techniques but can be overcome by the introduction of kits that are easy to assemble. Additionally, they are lightweight, inexpensive to maintain, programmable through the standard interfaces provided on most computers, and yet, offer sufficient extensibility to create and experiment with a wide range of agent behaviors. At the same time, using robots also leads the students to an important conclusion about scalability: the real world is very different from a simulated world, which has been a long standing criticism of many well-known AI techniques.
We proposed a plan to develop identical robot building laboratories at both Bryn Mawr and Swarthmore Colleges that would allow us to integrate the construction of robots into our introductory AI courses. Furthermore, we hoped that these laboratories would encourage our undergraduate students to pursue honors theses and research projects dealing with the building of physical agents.
