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. 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 Pro: Simple model Con: Cannot explain transfer Competing Strategies (Fum & Stocco, unpublished) Model has 6 competing strategies.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 targets But: 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 System Dynamics: P t = 2 W t - P t-1 + Random Factor (-1/0/1) 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.