I2RP/OPTIMA

1
I2RP/OPTIMA Optimal Personal Interface by
Man-Imitating Agents
Artificial intelligence Cognitive Engineering
Institute, University of Groningen, Grote
Kruisstraat 2/1, 9712 TS Groningen, the
Netherlands, http//www.ai.rug.nl drs. Judith
D.M. Grob (PhD student) dr. Niels A. Taatgen
(supervisor) dr. Lambert Schomaker (promotor)
? Project Objective

? Current Work
? Future Plans

• Problem
• With software becoming more and more complex,
  software design geared towards the average user
  is insufficient, as different users have
  different needs.
• Users differ in goals, experience, interests,
  knowledge.
• Possible Solution Let the system maintain a
  cognitive model of the user, which performs the
  role of an intelligent agent that can inform the
  interface on user-relevant adaptations.

Sugar Factory Experiment (Berry Broadbent,
1984)
Task Keep during two phases of 40 trials, the
production P of a simulated sugar factory at a
target value T, by allocating the right number
of workers W to the job.
System Dynamics Pt 2 Wt - Pt-1 Random
Factor (-1/0/1)

• Findings
• Participants are better at reaching 3 than 9
• Implicit learning participants improve but
  cannot verbalise knowledge
• Transfer change of target doesnt effect
  learning

Two Computational Models (in ACT-R)

• Instance Model
• (Taatgen Wallach, 2002)
• Model stores instances of experiences with
  trials. It retrieves these as examples to solve
  new trials.
• Pro Simple model
• Con Cannot explain transfer

• Competing Strategies
• (Fum Stocco, unpublished)
• Model has 6 competing strategies. The successful
  ones are used more frequent over time.
• Pro Models all effects
• Con Task-dependent strategies

Gain a better understanding of what happens when
people get more skilled at operating a complex
system, such as a software program.
Objective To come to a methodology for the
development of adaptive user interfaces, using
the Cognitive Architecture ACT-R (Anderson, 2002)
as a modeling tool
References
Our Analogy Model (in ACT-R)

• Contains simple, task independent analogy rules,
  which search for
• common patterns e.g. repetition of values.
• Model applies analogy rules to instances
  retrieved from memory and
• thus forms task-specific strategies to solve
  the task.

• Anderson, J. R. (2002). Spanning seven orders of
  magnitude A challenge for cognitive modeling.
  Cognitive Science, 26.
• Berry, D.C., Broadbent, D.E. (1984). On the
  relationship between task performance and
  associated verbalizable knowledge. The Quarterly
  Journal of Experimental Psychology, 36, 209-231
• Fum, D. Stocco, A. (unpublished). Instance vs.
  rule based learning in controlling a dynamic
  system. Submitted to ICCM 2003.
• Taatgen, N.A., Wallach, D. (2002). Whether
  skill acquisition is rule or instance based is
  determined by the structure of the task.
  Cognitive Science Quarterly, 2, 163-204.

• Three research phases

• Findings
• Learning
• Difference between targets
• But
• No transfer
• Values are too high

• Possible areas of adaptation
• help function
• display of menus

• Next
• Why doesnt the model apply newly formed rules
  more often?
• Let model forget through decaying activation in
  memory
• Experiment with relative representations

634.000.002 (I2RP)