On the Use of Intelligent Agents as Partners in Training Systems for Complex Tasks*

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On the Use of Intelligent Agentsas Partners in
Training Systemsfor Complex Tasks

• Thomas R. Ioerger, Joe Sims, Richard Volz
• Department of Computer Science
• Texas AM University
• Judson Workman, Wayne Shebilske
• Department of Psychology
• Wright State University

Funds provided by a MURI grant through DoD/AFOSR.
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Complex Tasks, and the Needfor new Training
Methods

• Complex tasks (e.g. operating machinery)
• multiple cognitive components (memory,
  perceptual, motor, reasoning/inference...)
• novices feel over-whelmed
• limitations of part-task training
• automaticity vs. attention management
• Role for intelligent agents?
• can place agents in simulation environments
• need guiding principles to promote learning

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Previous Work Partner-Based Training

• AIM (Active Interlocked Modeling Shebilske,
  1992)
• trainees work in pairs (AIM-Dyad)
• each trainee does part of the task together
• importance of context (integration of responses)
• can produce equal training, 100 efficiency gain
• co-presence/social variables not required
• trainees placed in separate rooms
• correlation with intelligence of partner
• Bandura, 1986 modeling

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Automating the Partner with an Intelligent Agent

• Hypothesis Would the training be as effective if
  the partner were played by an intelligent agent?
• Important pre-requisite a CTA (cognitive task
  analysis)
• a hierarchical task-decomposition allows
  functions to be divided in a natural way
  between human and agent partners

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Space Fortress Laboratory Task

• Representative of complex tasks
• has similar perceptual, motor, attention, memory,
  and decision-making demands as flying a fighter
  jet
• continuous control navigation with joystick,
  2nd-order thrust control
• discrete events firing missles, making bonus
  selections with mouse
• must learn rules for when to fire, boundaries...
• Large body of previous studies/data
• Multiple Emphasis on Components (MEC) protocol
• transfers to operational setting (attention mgmt)

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P
M
I
MOUSE BUTTONS
JOYSTICK
THE FORTRESS
FORTRESS SHOT
SHIP

BONUS AVAILABLE
A MINE
PNTS CNTRL VLCTY VLNER IFF
INTRVL SPEED SHOTS 200
100 119 0
W 90
70
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Implementation of a Partner Agent

• Implemented decision-making procedures for
  automating mouse and joystick
• Added if-then-else rules in C source code
• emulate Decision-Making with rules
• Agent simple, but satisfies criteria
• situated, goal-oriented, autonomous
• First version of agent played too perfectly
• Make it play realistically by adding some
  delays and imprecision (e.g. in aiming)

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Agent Finite-State Diagrams
Handling the Fortress
Handling Mines
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Experiment 1

• Hypothesis Training with agent improves final
  scores
• Protocol
• 10 sessions of 10 3-minute trials each (over 4
  days)
• each session 1/2 hour 8 practice trials, 2 test
  trials
• Groups
• Control (standard instructionspractice)
• Partner Agent (instructionspractice, alternate
  mouse and joystick between trainee and agent)
• Participants
• 40 male undegrads at WSU
• lt20 hrs/wk playing video games

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Results of Expt 1
Difference in final scores was significant at
plt0.05 level by paired T-test (with dof38)
t2.33gt2.04
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Breakdown of Scores
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Effect of Level of Simulated Expertise of Agent?

• Results of Expt 1 raises follow-up question What
  is the effect of the level of expertise simulated
  by the agent?
• Can make the agent more or less accurate.
• Recall correlation with partners intelligence
• Is it better to train with an expert? or perhaps
  with a partner of matching skill-level?...
• novices might have trouble comprehending experts
  strategies since struggling to keep up

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Experiment 2

• Hypothesis Different skill-levels of agent
  affect trainees performance improvement
• Similar design as Expt 1, except 4 Groups
• Control, Novice agent, Intemediate agent, Expert
• Adjust skill-level of agent by fine-tuning
  randomness parameters (shot timing, aiming
  accur., IFF mistakes)
• Gauge to skill levels target groups (empirical)

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Results of Expt 2
Conclusion Training with an expert partner agent
is best.
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Lessons Learned for Future Applications

• Principled approach to using agents in training
  systems as partners - cognitive benefits
• Requires CTA
• best if high degree of de-coupling
• if greater interaction, agent might have to
  cooperate with human by interpreting and
  responding to apparent strategies
• Desiderata for Agent
• Correctness
• Consistency (necessary for modeling)
• Realism (how to simulate human errors?)
• Exploration (errors lead to unusual situations)