Undergraduate Artificial Intelligence Research Articles

Research on Teaching Reform of Artificial Intelligence Course Based on CDIO

In view of the problem of how to set up general undergraduate artificial intelligence courses, on the basis of carefully combing and summarizing years of teaching exploration and practice, it is proposed to set Artificial Intelligence (AI) courses in the lower grades of the university. Taking the teaching practice carried out by Liaoning Institute of Science and Engineering as an example, the “12365” principle is proposed based on the CDIO concept, and corresponding teaching reform and practice are carried out.

Teaching Undergraduate Artificial Intelligence Classes: An Experiment with an Attendance Requirement

We report on an experiment that we performed when we taught the undergraduate artificial intelligence class at the University of Southern California. We taught it – under very similar conditions – once with and once without an attendance requirement. The attendance requirement substantially increased the attendance of the students. It did not substantially affect their performance but decreased their course ratings across all categories in the official course evaluation, whose results happened to be biased toward the opinions of the students attending the lectures. For example, the overall rating of the instructor was 0.89 lower (on a 1-5 scale) with the attendance requirement and the overall rating of the class was 0.85 lower. Thus, the attendance requirement, combined with the policy for administering the course evaluation, had a large impact on the course ratings, which is a problem if the course ratings influence decisions on promotions, tenure, and salary increments for the instructors but also demonstrates the potential for the manipulation of course ratings.

UTBot: A Virtual Agent Platform for Teaching Agent System Design

We introduce UTBot, a virtual agent platform for teaching agent system design. UTBot implements a client for the Unreal Tournament game server and Gamebots system. It provides students with the basic functionality required to start developing their own intelligent virtual agents to play autonomously UT games. UTBot includes a generic agent architecture, CAA (Context-sensitive Agent Architecture), a domain-specific world model, a visualization tool, several basic strategies (represented by internal modes and internal behaviors), and skills (represented by external behaviors). The CAA architecture can support complex long-term behaviors as well as reactive short-term behaviors. It also realizes high context-sensitivity of behaviors. We also discuss our experience using UTBot as a pedagogical tool for teaching agent system design in undergraduate Artificial Intelligence course.

Unifying Undergraduate Artificial Intelligence Robotics: Layers of Abstraction over Two Channels

From a computer science and artificial intelligence perspective, robotics often appears as a collection of disjoint, sometimes antagonistic subfields. The lack of a coherent and unified presentation of the field negatively affects teaching, especially to undergraduates. This article presents an alternative synthesis of the various subfields of AI robotics and shows how these traditional subfields fit into the whole. Finally, it presents a curriculum based on these ideas.

Three fun assignments for an Artificial Intelligence class

Three creative assignments are discussed that are geared toward an undergraduate Artificial Intelligence curriculum. The modified Turing Test project has students conversing with online chat bots, as well as with other classmates. All conversations are posted on the web and classified as to who the was speaking to, another human or a computer. The sixteen puzzle project requires students to compare the performance of two different heuristics used in solving the sixteen puzzle problem. The final assignment uses LEGO( handy-board based robots to teach the concepts of neural networks.

An intelligent agent approach for teaching neural networks using LEGO® handy board robots

In this article we describe a project for an undergraduate artificial intelligence class. The project teaches neural networks using LEGO® handy board robots. Students construct robots with two motors and two photosensors. Photosensors provide readings that act as inputs for the neural network. Output values power the motors and maintain the robot along the designated path. In doing this project, students come to realize the difference between training a neural network and the trained neural network. The fun factor associated with this project has encouraged students to elect artificial intelligence as part of their course of study.

A distributed task environment for teaching artificial intelligence with agents

It is not uncommon to teach Artificial Intelligence (AI) by asking students to implement agents that embody intelligent behavior. This helps students gain a fuller understanding of the many concepts taught in the course. There are two issues with this approach that deserve attention. First, students come into an AI course knowing how to program in different languages and having different levels of programming ability. Second, it's useful for the students to have a single task environment for all of the agents they program. A solution to both issues lies in a distributed system where the agents are clients communicating with a server that handles a configurable task environment. This allows the students to program their agents in any language and on any platform they desire, so long as they can communicate with the task environment server. If the task environment can be configured to provide additional levels of complexity and difficulty, this allows students to program at a level they are comfortable with. They can then challenge themselves by incorporating more advanced capabilities into their agents. This paper presents just such a distributed and configurable task environment that was developed for an undergraduate AI course.

Teaching neural networks using LEGO handy board robots in an artificial intelligence course

In this paper we propose a novel method for teaching neural networks with back propagation in an undergraduate Artificial Intelligence course. We use an agent based approach in the course, as outlined in the textbook Artificial Intelligence A Modern Approach by Stuart Russell and Peter Norvig [7]. The students build a robot agent whose task is to learn path-following behavior with a neural network. Robot agents are constructed from standard LEGO pieces and use the MIT Handy Board as a controller.

Undergraduate AI and its non-imperative prerequisite

This position paper presents, we believe, a strong case for including in an undergraduate Computer Science (CS) program a required course in non-imperative programming. Such a course is motivated by the need to provide a uniform and adequate background for a course in Artificial Intelligence (AI). We make no claim about graduate AI courses. We survey the recommendations of the ACM-IEEE Joint Curriculum Task Force for an undergraduate course in AI and argue that some of the "dilemmas" of teaching introductory AI courses can be resolved by changes in the core CS curriculum. The argument for such a course is the focus of the paper; we also offer some ancillary remarks on other matters relating to teaching AI at the introductory level.

A repository that supports teaching and cooperation in the introductory AI course

This paper describes the development of FLAIR (Flexible Learning with an Artificial Intelligence Repository), a repository of educational material and of a highly visual computing environment for use in laboratories associated with the introductory undergraduate Artificial Intelligence (AI) course. This repository supports sharing of pedagogic material and of development tools, and cooperation in their use, while allowing diversity in content and in use at different institutions. Thus the development of the repository has stressed the production of system tools, extensible object-oriented libraries, and strong programming frameworks. Some modules currently available are on Search and Automated Reasoning. Examples of the presentation techniques used are conceptual maps, hypertext, and graphic animations of algorithms. Initial experience in the use of the repository in teaching the introductory AI course is taking place in the 1993/94 academic year.

Designing laboratory modules for novices in an undergraduate AI course track

A current joint project between three institutions in Switzerland has as its goal to create Artificial Intelligence (AI) software in teaching principles of AI at the University level. The modules of this project, the Portable AI Lab (PAIL), illustrate basic concepts of Artificial Intelligence in a uniform and self-contained manner. This paper discusses the design considerations that were adopted in order to make the presentation of this material easier for novice students.