Multiagent Systems as a Team Member: Presented at the 9th International Technology, Knowledge, and Society Conference in Vancouver, Canada. Presented by Common Ground Publishing - 2013.
Finding The Voice of A Virtual Community of Practice
Mas teams slides_final
1. Multiagent Systems as a Team Member
John R. Turner
The University of North Texas
College of Information
Department of Learning Technologies
www.lt.unt.edu
Blog: johnrturnerhptresource@blogspot.com
Twitter: @ johnrturnerHPT
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2. “As the complexity of the workplace continues to
grow, organizations increasingly depend on teams.”
(Salas, Cooke, & Rosen, 2008, p. 540)
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3. “Knowledge is created through social interactions, interactions
between implicit and explicit knowledge, known as knowledge
conversion.”
(Nonaka, von Krogh, & Voelpel, 2006)
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5. Discussing unshared knowledge contributes
to a team’s collective knowledge base while
discussing shared knowledge does not.
I ER
RR
(Larson, Foster-Fishman, Keys, 1994)
B A
D GE
LE
N OW
K
R ED
S HA Shared knowledge is more likely to be discussed
UN during discussion and decision-making activi-
ties. When unshared knowledge is discussed it
is often not considered.
(Bromme et al., 2005; Wittenbaum et al., 1999)
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6. RESEARCH QUESTIONS
HOW DO YOU INCREASE DISCUSSION AND CONSIDER-
ATION OF UNSHARED KNOWLEDGE ? ????
HOW DO YOU TRANSFER UNSHARED KNOWLEDGE TO
SHARED KNOWLEDGE FOR MORE EFFECTIVE TEAM
DECISION MAKING ? ????
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9. TEAM CONSTRUCTS FROM RESEARCH
WEB 2.0 & 3.0 TECHNOLOGIES
TRANSACTIVE MEMORY
SYSTEMS
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10. TEAM CONSTRUCTS FROM RESEARCH
TEAM TRAINING
COGNITIVELY CENTRAL
GROUP MEMBERS
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11. INTELLIGENT / MULTIAGENT SYSTEMS
In this age of complexity with an exponential growth of
data it is difficult to process information of decision-
making tasks.
(Hackman, 2011; Sycara et al., 1996)
Intelligent software agents are one means to address
this issue of complexity.
(Hackman, 2011; Sycara et al., 1996)
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12. INTELLIGENT SOFTWARE AGENT - TASKS
• Locating and accessing information from various on-line in-
formation sources
• Resolving inconsistencies in the retrieved information
• Filtering away irrelevant or unwanted information
• Integrating information from heterogeneous information
sources
• Adapting over time to human users’ information needs and
the shape of the infosphere
(Sycara et al., 1996, p. 36)
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13. MULTIAGENT SYSTEMS (MAS)
MAS are composed of
a number of individual
intelligent agents.
MAS are intelligent due to their capability to learn,
making them attractive during problem solving and
decision making activities.
(Iantovics, 2010)
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14. ELECTRIC ELVES
Electric Elves provided the following
unique functions:
• the software agent acted on behalf of the
human user,
• the software agent made decisions with
no input from the human user, and
• the software agents’ decision was based
on input from the human user.
(Chalupsky et al., 2002)
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15. MemeXerciser
Developed by Matt Lee from Carnegie Mellon
...“emerging class of intelligent devices meant to provide support
for people with cognitive decline from Alzheimers and other con-
ditions” (Carroll, 2010)
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16. MULTIAGENT SYSTEMS -cont.-
Research conducted by Fan, Chen, and Yen (2010) using human-
agent pairs showed that human-agent pairs were better able to
“estimate other team members’ cognitive load allow[ing] them to
share the needed information with the right party at the right time.”
(p. 117)
MAS have the potential to consider shared and unshared knowledge
equally, resulting in better decision making abilities.
This leads us to the following
Team-MAS Model:
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17. Team Member
Multiagent System
(TM-MAS)
Psychological
Safety *
TM #1 - MAS
Team
Cohesion *
TM #N - MAS TM #2 - MAS
Team
Membership *
Team Multiagent System
Team
Conflict *
TM #4 - MAS TM #3 - MAS
Web 2.0 & 3.0
Technologies *
Transactive
Memory Systems *
Team Training *
Cognitive Central
Group Members *
* Individual Intelligent Agent
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19. References:
Bromme, R., Hesse, F. W., & Spada, H. (2005). Barriers, biases and opportunites of communication and cooperation with computers: Introduc-
tion and overview. In R. Bromme, F. W. Hesse, & H. Spada (Eds.), Barriers and biases in computer-mediated knolwedge communication - and
how they may be overcome (pp. 1-14). New York: Springer.
Carroll, C. (September 27, 2010). Technology solutions for age-related ills. AARP Bulletin. Retreived from http://www.aarp.org/technology/in-
novations/info-09-2010/techno_solutions_for_agerelated_ills.html
Chalupsky, H. , Gil, Y., Knoblock, C. A., Lerman, K., Oh, J., Pynadath, D. V., Russ, T. A., & Tambe, M. (2002). Electric elves: Agent technology
for supporting human organizations. AI Magazine, 23(2), 11-24. Retrieved from http://www.aaai.org/Library/magazinelibrary.php
Hackman, R. J. (2011). Collaborative Intelligence: Using teams to solve hard problems. San Francisco, CA: Berrett-Koehler.
Iantovics, B. (2010). Cognitive medical multiagent systems. BRAIN, Broad Research in Artificial Intelligence and Neuroscience, 1, 12-21. Re-
trieved from http://www.broadresearch.org
Larson, J. R., Jr., Foster-Fishman, P. G., & Keys, C. B. (1994). Discussion of shared and unshared information in decision-making groups. Jour-
nal of personality and social psychology, 67(3), 446-462. Retrieved from http://www.apa.prg.pubs/journals/psp/index.aspx
Lee, M., & Dey, A. (2008, July). Lifelogging Memory Aid for People with Alzheimer’s Disease. Retrieved from www.cs.cmu.edu/~mllee/mem.
html
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20. References -cont.-
Nonaka, I., von Kroght, & Voelpel, S. (2006). Organizational knowledge creation theory: Evolutionary paths and future advances. Organization
Studies, 27(8), 1179-1208. doi: 10.1177/017084060606066312
Salas, E., Cooke, N. J., & Rosen, M. A. (2008). On teams, teamwork, and team performance: Discoveries and developments. Human Factors: The
Journal of the Human Factors and Ergonomics Society, 50(3), 540-547. doi: 10.1518/001872008X288457
Schreiber, M., & Englemann, T. (2010). Knowledge and information awareness for initiating transactive memory system processes of computer-
supported collaborating ad hoc groups. Computers in Human Behavior, 26, 1701-1709. doi: 10.1016/j.chb.2010.06.019
Sycara, K., Pannu, A., Williamson, M., Zeng, D., & Decker, K. (1996). Distributed intelligent agents. IEEE expert, 11(6), 36-46. doi:
10.1109/64.546581
Wittenbaum, G. M., Hubbell, A. P., & Zuckerman, C. (1999). Mutual enhancement: Toward an understanding of the collective preference for
shared information. Journal of Personality and Social Psychology, 77(5), 967-978. Retrieved from http://www.apa.org/pubs/journals/psp/index.aspx
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21. Figures:
SLIDE #6: Question Mark - by Danilo Rizzuti at www.freedigitalphotos.net
SLIDE #7: Psychological Safety - by digitalart at www.freedigitalphotos.net
SLIDE #7: Team Cohesion - by idea go at www.freedigitalphotos.net
SLIDE #8: Team Membership - by Danilo Rizzuti at www.freedigitalphotos.net
SLIDE #8: Team Conflict - by coodesign at www.freedigitalphotos.net
SLIDE #9: Web 2.0 & 3.0 Technologies - by digitalart at www.freedigitalphotos.net
SLIDE #9: Transactive Memory Systems - by renjith Krishman at www.freedigitalphotos.net
SLIDE #10: Team Training - by David Castillo Dominici at www.freedigitalphotos.net
SLIDE #10: Cognitive Central Group Member - by jscreationzs at www.freedigitalphotos.net
SLIDE #13: Multiagent Systems, blocks - by renjith Krishnan at www.freedigitalphotos.net
SLIDE 14: Electric Elf
Chalupsky, H., Gil, Y., Knoblock, C. A., Lerman, K., Oh, J., Pynadath, D. V., Russ, T. A., & Tambe, M. (2001). Electric Elves: Applying agent technol-
ogy to support human organizations. American Association for Artificial Intelligents. Retrieved from www.isi.edu/e-elves/papers/iaai2000.pdf
SLIDE #15: MemeXerciser
Carroll, C. (September 27, 2010). Technology solutions for age-related ills. AARP Bulletin. Retreived from http://www.aarp.org/technology/innova-
tions/info-09-2010/techno_solutions_for_agerelated_ills.html
SLIDE #16: Baloons - by maple at www.freedigitalphotos.net
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