article

A distributed task environment for teaching artificial intelligence with agents

Authors:

John M. D. Hill,

Kenneth L. AlfordAuthors Info & Claims

ACM SIGCSE Bulletin, Volume 36, Issue 1

Pages 224 - 228

https://doi.org/10.1145/1028174.971379

Published: 01 March 2004 Publication History

Get Access

Abstract

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.

References

[1]

"Welcome to the Soar Home Page," University of Michigan, Available at http://www.eecs.umich.edu/~soar/, accessed {September 10, 2003}.

Google Scholar

[2]

Gasser, L., "Large-Scale Concurrent Computing in Artificial Intelligence Research," Third Conference on Hypercube Concurrent Computers and Applications, Pasadena, California (January 19-20, 1988), 1342--1351.

Digital Library

Google Scholar

[3]

Paine, J., "Using Java and the Web as a Front-End to an Agent-Based Artificial Intelligence Course," OxTALENT, (February, 2000), Available at http://www.j-paine.org/publications.html, accessed {September 10, 2003}.

Google Scholar

[4]

Rose, J. R., Huhns, M. N., Roy, S. S., and Turkett, W. H., "An Agent Architecture for Long-Term Robustness," First International Joint Conference on Autonomous Agents and Multiagent Systems, Bologna, Italy (July 15-19, 2002), 1149--1156.

Digital Library

Google Scholar

[5]

Russell, S. and Norvig, P., Artificial Intelligence: A Modern Approach, Second Edition, Upper Saddle River, New Jersey: Prentice Hall (2002).

Digital Library

Google Scholar

[6]

Shang, Y., Shi, H., and Chen, S.-S., "Agent Technology in Computer Science and Engineering Curriculum," Fifth Annual SIGCSE/SIGCUE ITiCSE Conference on Innovation and Technology in Computer Science Education, Helsinki, Finland (July 11-13, 2000), 120--123.

Digital Library

Google Scholar

[7]

Shang, Y., Shi, H., and Chen, S.-S., "An Intelligent Distributed Environment for Active Learning," ACM Journal of Educational Resources in Computing, Vol. 1, No. 2es, (Summer, 2001), Article 4.

Digital Library

Google Scholar

[8]

Siemer, J. and Angelides, M. C., "Evaluating Intelligent Tutoring with Gaming-Simulation," 1995 Winter Simulation Conference, Arlington, Virginia (December 3-6, 1995), 1367--1383.

Digital Library

Google Scholar

[9]

St. Amant, R. and Zettlemoyer, L. S., "The User Interface as an Agent Environment," Fourth International Conference on Autonomous Agents, Barcelona, Spain (June 3-7, 2000), 483--490.

Digital Library

Google Scholar

[10]

Tambe, M. J., Jones, W. L., Jones, R., Koss, F., Laird, J. E., Rosenbloom, P. S., and Schwamb, K., "Intelligent Agents for Interactive Simulation Environments," AI Magazine, Vol. 16, No. 1, (1995), 15--39.

Google Scholar

[11]

Yob, G., "Hunt the Wumpus," Creative Computing, (Sep-Oct, 1975), 51--54.

Google Scholar

Cited By

View all

Ng DLee MTan RHu XDownie JChu S(2022)A review of AI teaching and learning from 2000 to 2020Education and Information Technologies10.1007/s10639-022-11491-w28:7(8445-8501)Online publication date: 21-Dec-2022
https://dl.acm.org/doi/10.1007/s10639-022-11491-w
Kerins J(2015)Developing Critical Insights into Artificial IntelligenceInnovation in Teaching and Learning in Information and Computer Sciences10.11120/ital.2005.040300034:3(1-13)Online publication date: 15-Dec-2015
https://doi.org/10.11120/ital.2005.04030003
Hansen DBruce JHarrison D(2007)Give students a clueACM SIGCSE Bulletin10.1145/1227504.122732639:1(44-48)Online publication date: 7-Mar-2007
https://dl.acm.org/doi/10.1145/1227504.1227326
Show More Cited By

Index Terms

A distributed task environment for teaching artificial intelligence with agents
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Intelligent agents
2. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education

Recommendations

A distributed task environment for teaching artificial intelligence with agents
SIGCSE '04: Proceedings of the 35th SIGCSE technical symposium on Computer science education

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 ...
Artificial intelligence
Abstract
Artificial intelligence (AI) is the Science and Engineering domain concerned with the theory and practice of developing systems that exhibit the characteristics we associate with intelligence in human behavior. Starting with a brief history of ...
Hybrid Bionic Cognitive Architecture for Artificial General Intelligence Agents
Abstract
The article describes the author’s proposal on cognitive architecture for the development of a general-level artificial intelligent agent («strong» artificial intelligence). New principles for the development of such an architecture are proposed — ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

ACM SIGCSE Bulletin Volume 36, Issue 1

March 2004

501 pages

ISSN:0097-8418

DOI:10.1145/1028174

Issue’s Table of Contents

SIGCSE '04: Proceedings of the 35th SIGCSE technical symposium on Computer science education
March 2004
544 pages
ISBN:1581137982
DOI:10.1145/971300
Conference Chairs:
Dan Joyce
Villanova University
,
Deborah Knox
The College of New Jersey
,
Program Chairs:
Wanda Dann
Ithaca College
,
Tom Naps
University of Wisconsin - Oshkosh

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 March 2004

Published in SIGCSE Volume 36, Issue 1

Check for updates

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
1,286
Total Downloads

Downloads (Last 12 months)10
Downloads (Last 6 weeks)0

Reflects downloads up to 26 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Ng DLee MTan RHu XDownie JChu S(2022)A review of AI teaching and learning from 2000 to 2020Education and Information Technologies10.1007/s10639-022-11491-w28:7(8445-8501)Online publication date: 21-Dec-2022
https://dl.acm.org/doi/10.1007/s10639-022-11491-w
Kerins J(2015)Developing Critical Insights into Artificial IntelligenceInnovation in Teaching and Learning in Information and Computer Sciences10.11120/ital.2005.040300034:3(1-13)Online publication date: 15-Dec-2015
https://doi.org/10.11120/ital.2005.04030003
Hansen DBruce JHarrison D(2007)Give students a clueACM SIGCSE Bulletin10.1145/1227504.122732639:1(44-48)Online publication date: 7-Mar-2007
https://dl.acm.org/doi/10.1145/1227504.1227326
Hansen DBruce JHarrison DRussell IHaller SDougherty JRodger S(2007)Give students a clueProceedings of the 38th SIGCSE technical symposium on Computer science education10.1145/1227310.1227326(44-48)Online publication date: 7-Mar-2007
https://dl.acm.org/doi/10.1145/1227310.1227326

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

A distributed task environment for teaching artificial intelligence with agents

Artificial intelligence

Hybrid Bionic Cognitive Architecture for Artificial General Intelligence Agents