I2RP/OPTIMA

Optimal Personal Interface by Man-Imitating AgentsArtificial 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 PlansProblem•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.

Application

Agent

Agent controlsapplication

1.

Agent Application

User

Agent learnswith user

2.

Agent Application

User

Applicationadapts, basedon agent

3.

Possible areas of adaptation:•help function

•display of menu’s

Three research phases:

References

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.

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

Two Computational Models(in ACT-R)

634.000.002 (I2RP)

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Competing Strategies(Fum & Stocco, unpublished)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

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.

Findings:• Learning• Difference between targetsBut:• No transfer• Values are too high

Next:• Why doesn’t the model apply newly formed rules more often?• Let model forget through decaying activation in memory• Experiment with relative representations

Predictions by Analogy Model

0

5

10

15

20

25

phase1 phase2

Successes

3-39-93-99-3

System Dynamics: Pt = 2 Wt - Pt-1 + Random Factor (-1/0/1)

Experimental Data (Fum & Stocco, unpublished)

0

2

4

6

8

10

12

14

phase1 phase2

Successes

3-3

9-9

3-9

9-3

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”

Gain a better understanding of what happens when people get more skilled at operating a complex system, such as a software program.

• 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.

generalsubconscious

rules

declarativeconscious

rules

instances compilationthrough analogy

?

USER MODELS