Accelerating human-computer collaborative search through learning comparative and predictive user models

Interactive Evolutionary Algorithms (IEAs) have much potential for allowing a human user to guide a search algorithm, but have struggled to overcome the limitations of slow, easily-fatigued human users. Here we describe The Approximate User (TAU) system in which these limitations are overcome by using a model of the user's preferences - which are continuously built and refined during the search process - to drive the search algorithm. Two variations of a user-modeling approach are compared to determine if this approach can accelerate IEA search. The two user-modeling approaches compared are: 1. learning a classifier which correctly determines which of two designs is better; and 2. learning a model which predicts a fitness score. Rather than having people do the user-testing, we propose the use of a simulated user as an easier means to test IEAs. Both variants of the TAU IEA are compared against a basic IEA and it is shown that TAU is up to 2.7 times faster and 15 times more reliable at producing near optimal results. In addition, we see TAU as a step toward building a more general Human-Computer Collaborative system.