I2RP/OPTIMA Optimal Personal Interface by Man-Imitating Agents

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 doesn’t 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 • Pro: Simple model • Con: Cannot explain transfer • Competing Strategies • (Fum & Stocco, unpublished) • Model has 6 competing strategies.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 toICCM 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 menu’s • Next: • Why doesn’t the model apply newly formed rules more often? • Let model forget through decaying activation in memory • Experiment with relative representations 634.000.002 (I2RP)