Introducing Multiagent Systems to Undergraduates through Games and Chocolate

The field of ―intelligent agents and multiagent systems‖ is maturing; no longer is it a special topic to be introduced to graduate students after years of training in computer science and many introductory courses in artificial intelligence. Instead, the time is ripe to introduce agents and multiagents directly to undergraduate students, whether majoring in computer science or not. This chapter focuses on exactly this challenge, drawing on the co-authors‘ experience of teaching several such undergraduate courses on agents and multiagents, over the last three years at two different universities. The chapter outlines three key issues that must be addressed. The first issue is facilitating students‘ intuitive understanding of fundamental concepts of multiagent systems; we illustrate uses of science fiction materials and classroom games to not only provide students with the necessary intuitive understanding but with the excitement and motivation for studying multiagent systems. The second is in selecting the right material — either science-fiction material or games — for providing students the necessary motivation and intuition; we outline several criteria that have been useful in selecting such material. The third issue is in educating students about the fundamental philosophical, ethical and social issues surrounding agents and multiagent systems: we outline course materials and classroom activities that allow students to obtain this ―big picture‖ futuristic vision of our science. We conclude with feedback received, lessons learned and impact on both the computer science students and non computer-science students.

[1]  C. Bonwell,et al.  Active learning : creating excitement in the classroom , 1991 .

[2]  Sid-Ahmed Selouani,et al.  Enhanced Speech-Enabled Tools for Intelligent and Mobile E-Learning Applications , 2010 .

[3]  Yujian Fu,et al.  Architecture-Centered Integrated Verification , 2011 .

[4]  Leslie Pack Kaelbling,et al.  Planning and Acting in Partially Observable Stochastic Domains , 1998, Artif. Intell..

[5]  Diana F. Gordon,et al.  Asimovian Adaptive Agents , 2000, J. Artif. Intell. Res..

[6]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.

[7]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[8]  C. Bonwell,et al.  Active Learning: Creating Excitement in the Classroom. ERIC Digest. , 1991 .

[9]  Roger Penrose,et al.  Précis of The Emperor's New Mind: Concerning computers, minds, and the laws of physics , 1990, Behavioral and Brain Sciences.

[10]  Y. J. Tejwani,et al.  Robot vision , 1989, IEEE International Symposium on Circuits and Systems,.

[11]  Gene Roddenberry,et al.  Star Trek - The Next Generation , 2011 .

[12]  Milind Tambe,et al.  Conflicts in teamwork: hybrids to the rescue , 2005, AAMAS '05.

[13]  Barbara Messing,et al.  An Introduction to MultiAgent Systems , 2002, Künstliche Intell..

[14]  A. DeBaets Enough: Staying Human in an Engineered Age , 2003 .

[15]  References , 1971 .

[16]  W. S. Reilly,et al.  Believable Social and Emotional Agents. , 1996 .

[17]  Berthold K. P. Horn Robot vision , 1986, MIT electrical engineering and computer science series.

[18]  Makoto Yokoo,et al.  Taming Decentralized POMDPs: Towards Efficient Policy Computation for Multiagent Settings , 2003, IJCAI.

[19]  J. Krutch,et al.  The Measure of Man , 1953 .

[20]  Michael L. Littman,et al.  Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes , 1997, UAI.

[21]  Hector J. Levesque,et al.  On Acting Together , 1990, AAAI.

[22]  John R. Searle,et al.  Minds, brains, and programs , 1980, Behavioral and Brain Sciences.

[23]  Misook Heo,et al.  Girls and Computers - Yes We Can!: A Case Study on Improving Female Computer Confidence and Decreasing Gender Inequity in Computer Science with an Informal, Female Learning Community , 2011 .

[24]  Ajantha Dahanayake Computer-Aided Method Engineering : Designing CASE Repositories for the 21st Century , 2001 .

[25]  Claudia V. Goldman,et al.  Transition-independent decentralized markov decision processes , 2003, AAMAS '03.

[26]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.

[27]  Stacy Marsella,et al.  A step toward irrationality: using emotion to change belief , 2002, AAMAS '02.