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PhD Thesis - The University of Sydney

PhD Thesis - The University of Sydney

DECLARATIONI hereby

DECLARATIONI hereby declare that this submission is my own work and that, to the bestof my knowledge and belief, it contains no material previously publishedand written by another person nor material which to a substantial extent hasbeen accepted for the award of any other degree or diploma of theuniversity or another institute of higher learning, except where dueacknowledgement has been made in the text.VISHAL SINGH28 August 20092

AcknowledgementThis thesis has been an enriching experience. In the process of conducting my research, I have learntfrom my interactions with a number of people, and by observing the research activities of my peers andthe broader research community. In that sense, this research is as much a result of social learning as itis a product of a scholarly effort.I am particularly thankful to my supervisors, Dr. Andy Dong and Prof. John Gero for their constantsupport and guidance. By now, Andy seems to have a well-developed mental model of me, which heeffectively used to keep me on track, and shepherd me whenever I tended to deviate. His enthusiasm,guidance and insightful comments have been critical to my research. John has been instrumental inshaping my interest in agent-based modelling, and his passion for design research is contagious. I havehad a great time at The University of Sydney, developing friendships with many, especially Somwrita,Nick, Kaz, Ning, Jerry and Lucila. I am also thankful to Rob for his timely inputs on modelimplementation.Embarking on a PhD research by itself needs motivation and interest. In that respect, I am thankfulto my teachers at the Indian Institute of Science (IISc), especially Prof. Amaresh Chakrabarti, Prof. B.Gurumoorthy and Dr. Dibakar Sen. Their knowledge and humility inspired me through out my stay atIISc.I have been equally lucky to have an amazing family, which made me what I am. A fair bit of me isa reflection of my siblings, Pankaj and Niru, and their love and support has also contributed to thiswork in many ways. The years of my PhD candidature have also seen pleasant additions to my family,and Shantanu (my BIL) and Rukmini (my SIL) have also been a constant support. Gargi, my niece, istoo young to read this at the moment, but seeing her grow and demonstrate amazing learning skills hasbeen a lovely source of fun and excitement for the last year and a half. But above all, I can never begrateful enough to have the parents that I have. This thesis is a tribute to their years of efforts andsacrifices.3

  • Page 1: Computational Studies on the Role o
  • Page 5 and 6: Table of contentsChapter 1 Introduc
  • Page 7 and 8: 6.1.2 Calculating the value of TMM
  • Page 9 and 10: Table of FiguresFigure 2.1: Types o
  • Page 11 and 12: Table of TablesTable 3.1: Matrix of
  • Page 13 and 14: L TMASNR-AgentN Ag N AN Tk N TN Tp
  • Page 15 and 16: Chapter 1IntroductionLearning is a
  • Page 17 and 18: make assumptions (attributions) abo
  • Page 19 and 20: 1.1.1 Conceptual motivationThe rese
  • Page 21 and 22: Control, flexibility and scalabilit
  • Page 23 and 24: 1.4 Research claims, contributions
  • Page 25 and 26: grounded in the folk theory of mind
  • Page 27 and 28: adaptive than imitative learning. I
  • Page 29 and 30: physically. Such teams may also dif
  • Page 31 and 32: Many work teams are organized into
  • Page 33 and 34: Since this research primarily focus
  • Page 35 and 36: Routine tasks often have unique sol
  • Page 37 and 38: to efficiently utilize each other
  • Page 39 and 40: Hence, there should also be an inde
  • Page 41 and 42: identify popular agent architecture
  • Page 43 and 44: maintenance, execution and action,
  • Page 46 and 47: task, process and context mental mo
  • Page 48 and 49: However, higher busyness levels sho
  • Page 50 and 51: TeamPerformanceAll learning modesPa
  • Page 52 and 53:

    grouped into social cliques, and lo

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    the “busyness levels vs. levels o

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    fair chance to observe the interact

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    Figure 3.14: Hypothesized correlati

  • Page 60 and 61:

    Table 3.1: Matrix of hypotheses bei

  • Page 62 and 63:

    TFTSis organized into task-based su

  • Page 64 and 65:

    Project-based teams provide flexibi

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    Such scenarios are common when memb

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    agents need to know is already know

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    η1Value of overall solution, V s =

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    agent coordinating and evaluating t

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    Chapter 5Model ImplementationThe co

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    tasks such that multiple agents may

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    (once again it updates its TMM), it

  • Page 80 and 81:

    5.3.3 Using the TMM for task alloca

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    and A 10 ). That is, the retained a

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    agent has chosen a sub-task for rew

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    serve similar purposes as for the R

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    needs to coordinate the sub-tasks f

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    Figure 5.8: Capability of each agen

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    Since the agent constantly updates

  • Page 94 and 95:

    Table 5.1: Learning assumptions cor

  • Page 96 and 97:

    OntologyProtocolConversation IDRepl

  • Page 98 and 99:

    Go-De-register INFORM-IF -do- “Go

  • Page 100 and 101:

    duplication message, ignores the de

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    5.7 Implementing Client AgentThe Cl

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    than the proposal { y L R =1, y U R

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    Figure 5.18: Activity diagram for s

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    DFCONTROLLERCLIENTTEAM MEMBERSREGIS

  • Page 110 and 111:

    chooses as the lead agent. If the s

  • Page 112 and 113:

    Chapter 6Simulation Details and Res

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    5.3.4). SD is the standard deviatio

  • Page 116 and 117:

    Moreland et al. (1998) and Ren et a

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    observation (IO)- - √PI + Taskobs

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    CliqueCliqueBs0 1_a Grp_1 Grp_1 Bs0

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    Bs8 T mb_a [2 6], T mb_c [2 9] Grp_

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    75 31.75 6.80 15.82 3.47 6.30 0.79P

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    Team structure and level of TMM for

  • Page 128 and 129:

    PersonalInteraction17 53.63 15.21 -

  • Page 130 and 131:

    100 73.07 9.85 72.13 13.66 69.40 6.

  • Page 132 and 133:

    Pattern of message exchange across

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    Chapter 7Research FindingsThis chap

  • Page 136 and 137:

    R PI+IO Sub-teams 100 3.1058 0.0104

  • Page 138 and 139:

    differences 28 in the effects of te

  • Page 140 and 141:

    PI R Flat (0, 17, 33, 50) / (50, 66

  • Page 142 and 143:

    25 17, 33, 50, 66, 83, 100 5 7775.0

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    These findings partially reject hyp

  • Page 146 and 147:

    flat teams as compared to the flat

  • Page 148 and 149:

    Figure 7.11: Team structure and bus

  • Page 150 and 151:

    Figure 7.13(a) shows that the incre

  • Page 152 and 153:

    Figure 7.14 illustrates that when t

  • Page 154 and 155:

    PI+TO 63.58 (

  • Page 156 and 157:

    The team performance is highest for

  • Page 158 and 159:

    Chapter 8Conclusions, limitations a

  • Page 160 and 161:

    hypotheses stating correlations for

  • Page 162 and 163:

    agents learn only from personal int

  • Page 164 and 165:

    teams working on non-routine tasks

  • Page 166 and 167:

    However, in order to model and test

  • Page 168 and 169:

    Implementing busyness as cognitive

  • Page 170 and 171:

    these attributes together in a sing

  • Page 172 and 173:

    22. Brooks, R. (1991). Intelligence

  • Page 174 and 175:

    78. Griffith, T., & Neale, M. A. (1

  • Page 176 and 177:

    131. Mabogunje, A. (2003). Towards

  • Page 178 and 179:

    183. Smyth, M. M., Collins, A. F.,

  • Page 180 and 181:

    BDI AgentBusyness levelBDI agents a

  • Page 182 and 183:

    2.2.1)Folk theory ofmindFIPA protoc

  • Page 184 and 185:

    NR-Agent Agent working on non-routi

  • Page 186 and 187:

    agent expected to perform the task.

  • Page 188:

    agents in the team can access detai

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