Transcript Document
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 Future Plans USER MODEL S 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. Experimental Data (Fum & Stocco, unpublished) 14 instances 12 Successes Problem •With software becoming more and more complex, software design geared towards the ‘average user’ is insufficient, as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. Current Work 10 3-3 9-9 3-9 9-3 8 6 4 compilation through analogy general subconscious rules 2 System Dynamics: Pt = 2 W t - Pt-1 + Random Factor (-1/0/1) 0 phase1 phase2 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 declarative conscious rules ? Two Computational Models (in ACT-R) Instance Model Competing Strategies (Taatgen & Wallach, 2002) 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of adaptive user interfaces, using the Cognitive Architecture ACT-R (Anderson, 2002) as a modeling tool” (Fum & Stocco, unpublished) Model stores instances of experiences with trials. It retrieves these as examples to solve new trials. • Pro: Simple model • Con: Cannot explain transfer 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. 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. Three research phases: Predictions by Analogy Model Application Agent Application Agent Application 25 Agent controls application Agent 2. 3. Agent learns with user User 20 Application adapts, based on agent User Successes 1. 15 10 5 Findings: • Learning • Difference between targets 3-3 But: 9-9 3-9 • No transfer 9-3 • Values are too high 0 phase1 Possible areas of adaptation: •help function •display of menu’s • • • • 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. phase2 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)