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Multiagent Systems as a Team Member
John R. Turner
The University of North Texas
College of InformationDepartment of Learning Technologies
www.lt.unt.edu
Blog: [email protected]
Twitter: @ johnrturnerHPT
“As the complexity of the workplace continues to grow, organizations increasingly depend on teams.”
(Salas, Cooke, & Rosen, 2008, p. 540)
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“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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Implicit Knowledge Unshared Knowledge (Unique Knowledge)
Explicit Knowledge Shared Knowledge
Knowledge Conversion
IND
IVID
UA
L K
NO
WLE
DG
E
TEA
M
KN
OW
LED
GE
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Discussing unshared knowledge contributes to a team’s collective knowledge base while discussing shared knowledge does not.(Larson, Foster-Fishman, Keys, 1994)
Shared knowledge is more likely to be discussed 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)
UNSHARED KNOWLEDGE BARRIER
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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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TEAM CONSTRUCTS FROM RESEARCH
PSYCHOLOGICAL SAFETY
TEAM COHESION
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TEAM CONSTRUCTS FROM RESEARCH
TEAM CONFLICT
TEAM MEMBERSHIP
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TEAM CONSTRUCTS FROM RESEARCH
TRANSACTIVE MEMORY SYSTEMS
WEB 2.0 & 3.0 TECHNOLOGIES
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COGNITIVELY CENTRAL GROUP MEMBERS
TEAM TRAINING
TEAM CONSTRUCTS FROM RESEARCH
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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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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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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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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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MemeXerciserDeveloped 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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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.
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This leads us to the following Team-MAS Model:
Psychological Safety *
Cognitive Central Group Members *
Team Cohesion *
Team Training *
Team Membership *
Transactive Memory Systems *
Web 2.0 & 3.0 Technologies *
Team Conflict *
Team Member Multiagent System
(TM-MAS)
* Individual Intelligent Agent
Team Multiagent System
TM #1 - MAS
TM #4 - MAS TM #3 - MAS
TM #2 - MASTM #N - MAS
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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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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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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 ElfChalupsky, 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: MemeXerciserCarroll, 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
