Unifying An Introduction To Artificial Intelligence Course Through Machine Learning Laboratory Experiences

Abstract

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Unifying an Introduction to Artificial Intelligence Course through Machine Learning Laboratory Experiences Ingrid Russell, Zdravko Markov, Todd Neller, Michael Georgiopoulos, Susan Coleman University of Hartford/Central Connecticut State University/Gettysburg College/University of Central Florida/University of Hartford Abstract This paper presents work on a collaborative project funded by the National Science Foundation that incorporates machine learning as a unifying theme to teach fundamental concepts typically covered in the introductory Artificial Intelligence courses. The project involves the development of an adaptable framework for the presentation of core AI topics. This is accomplished through the development, implementation, and testing of a suite of adaptable, hands-on laboratory projects that can be closely integrated into the AI course. Through the design and implementation of learning systems that enhance commonly-deployed applications, our model acknowledges that intelligent systems are best taught through their application to challenging problems. The goals of the project are to (1) enhance the student learning experience in the AI course, (2) increase student interest and motivation to learn AI by providing a framework for the presentation of the major AI topics that emphasizes the strong connection between AI and computer science and engineering, and (3) highlight the bridge that machine learning provides between AI technology and modern software engineering. In this paper we will present our approach, an overview of the project, and the hands-on laboratory modules. Our preliminary experiences incorporating these modules into our introductory AI course will also be presented. 1. Introduction An introductory Artificial Intelligence (AI) course provides students with basic knowledge of the theory and practice of AI as a discipline concerned with the methodology and technology for solving problems that are difficult to solve by other means. The importance of AI in the undergraduate computer science curriculum is illustrated by the Computing Curricula 2001 recommendation of ten core units in AI2. It is believed by many faculty members that an introductory AI course is challenging to teach because of the diverse and seemingly disconnected topics that are typically covered6. Recently, work has been done to address the diversity of topics covered in the course and to create a theme-based approach. Russell and Norvig present an agent-centered approach21. A number of faculty have been working to integrate Robotics into the AI course3,7,8,9. Proceedings of the 2005 American Society for Engineering Education Annual Conference & Exposition Copyright © 2005, American Society for Engineering Education