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PhD Thesis - The University of Sydney

PhD Thesis - The University of Sydney

4.1.2 Social learning in

4.1.2 Social learning in team environments .......................................................................... 654.1.3 TMM ............................................................................................................................ 664.1.4 Busyness....................................................................................................................... 674.1.5 Team familiarity........................................................................................................... 674.1.6 Task.............................................................................................................................. 684.1.6.1 Routine tasks............................................................................................................ 684.1.6.2 Non-routine tasks..................................................................................................... 684.1.6.3 Task handling approaches........................................................................................ 724.1.6.4 Task allocation and team knowledge....................................................................... 73Chapter 5 Model Implementation .................................................................................................... 745.1 Agent overview..................................................................................................................... 745.2 Overview of the simulation environment: ............................................................................ 755.3 Implementing the R-Agent (Agent working on routine tasks) ............................................. 775.3.1 Knowledge required for the R-Agents ......................................................................... 785.3.2 Implementation of TMM for the R-Agent: .................................................................. 795.3.3 Using the TMM for task allocation and handling: ....................................................... 805.3.4 Observing the change in TMM .................................................................................... 805.3.5 Reset TMM .................................................................................................................. 815.4 Implementing the NR-Agents (Agents working on non-routine task).................................. 825.4.1 Knowledge required for the NR-Agents ...................................................................... 855.4.2 Implementation of TMM for the NR-Agent: ............................................................... 875.4.3 Updating AMM and TMM: ......................................................................................... 885.4.4 Using the TMM for task allocation and handling: ....................................................... 905.4.5 Observing the change in TMM for the NR-Agents...................................................... 925.5 Implementing learning in agents........................................................................................... 935.6 Implementing agent interactions and observations............................................................... 955.7 Implementing Client Agent ................................................................................................ 1025.7.1 Bid selection process.................................................................................................. 1025.7.2 Receipt of task completion information..................................................................... 1045.8 Implementing the Simulation Controller ............................................................................ 1055.9 Description of simulation lifecycle..................................................................................... 1065.10 Computational model as the simulation environment......................................................... 109Chapter 6 Simulation Details anad Results.................................................................................. 1126.1 Experiments to validate the computational model:............................................................. 1126.1.1 Simulation set-up: ...................................................................................................... 1136

6.1.2 Calculating the value of TMM formed ...................................................................... 1136.1.3 Discussion of simulation results: ............................................................................... 1146.2 Experiments designed to test the research hypotheses........................................................ 1166.2.1 Details of experiments conducted:............................................................................. 1196.2.1.1 Experiments with routine tasks and busyness........................................................ 1196.2.1.2 Experiments with routine tasks and team familiarity ............................................ 1216.2.1.3 Experiments with non-routine tasks and busyness ................................................ 1216.2.1.4 Experiments with team familiarity and busyness .................................................. 1226.2.2 Simulation results....................................................................................................... 1236.2.2.1 Experiments with routine tasks and busyness level............................................... 1236.2.2.2 Experiments with non-routine tasks and busyness level........................................ 1266.2.2.3 Experiments with routine tasks and team familiarity ............................................ 1276.2.2.4 Experiments with non-routine tasks and team familiarity ..................................... 1296.2.2.5 Experiments with busyness and team familiarity .................................................. 132Chapter 7 Research Findings.......................................................................................................... 1347.1 Social learning modes, busyness level, and level of team familiarity: ............................... 1347.1.1 Learning modes, busyness level and team performance ............................................ 1347.1.2 Learning modes, busyness level and TMMs.............................................................. 1367.1.3 Learning modes, team familiarity and team performance.......................................... 1377.1.4 Team familiarity, busyness level and team performance........................................... 1417.2 Social learning modes and team structure: ......................................................................... 1437.2.1 Team structure, learning modes and team performance............................................. 1437.2.2 Team structure, learning modes and TMM formation ............................................... 1447.2.3 Team structure and efficiency of formed TMM......................................................... 1457.2.4 Team structure, busyness level and team performance.............................................. 1477.2.5 Team structure, busyness level and TMM formation ................................................ 1487.2.6 Team structure, team familiarity and team performance ........................................... 1497.3 Social learning and task types:............................................................................................ 1517.3.1 Task types, learning modes and team performance ................................................... 1517.3.2 Task types, busyness level and team performance..................................................... 1527.3.3 Task types, busyness level and TMM formation ....................................................... 1537.3.4 Task types, team familiarity and team performance .................................................. 1547.3.5 Task types, team structure and team performance ..................................................... 1557.3.6 Task types, team structure and TMM formation........................................................ 156Chapter 8 Conclusions, Limitations and Future works ............................................................... 1587

  • Page 1 and 2: Computational Studies on the Role o
  • Page 3 and 4: AcknowledgementThis thesis has been
  • Page 5: Table of contentsChapter 1 Introduc
  • 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
  • Page 54 and 55: 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

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    Table 3.1: Matrix of hypotheses bei

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    TFTSis organized into task-based su

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    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

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    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

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    Table 5.1: Learning assumptions cor

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    OntologyProtocolConversation IDRepl

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    Go-De-register INFORM-IF -do- “Go

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    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

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    chooses as the lead agent. If the s

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    Chapter 6Simulation Details and Res

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    5.3.4). SD is the standard deviatio

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    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

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    PersonalInteraction17 53.63 15.21 -

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    100 73.07 9.85 72.13 13.66 69.40 6.

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    Pattern of message exchange across

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    Chapter 7Research FindingsThis chap

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    R PI+IO Sub-teams 100 3.1058 0.0104

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    differences 28 in the effects of te

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

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

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    These findings partially reject hyp

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    flat teams as compared to the flat

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    Figure 7.11: Team structure and bus

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    Figure 7.13(a) shows that the incre

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    Figure 7.14 illustrates that when t

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    PI+TO 63.58 (

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    The team performance is highest for

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    Chapter 8Conclusions, limitations a

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    hypotheses stating correlations for

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    agents learn only from personal int

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    teams working on non-routine tasks

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    However, in order to model and test

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    Implementing busyness as cognitive

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    these attributes together in a sing

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    22. Brooks, R. (1991). Intelligence

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

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    131. Mabogunje, A. (2003). Towards

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

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    BDI AgentBusyness levelBDI agents a

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    2.2.1)Folk theory ofmindFIPA protoc

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    NR-Agent Agent working on non-routi

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    agent expected to perform the task.

  • Page 188:

    agents in the team can access detai

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