Learning Comparative User Models for Accelerating Human-Computer Collaborative Search

Interactive Evolutionary Algorithms (IEAs) are a powerful explorative search technique that utilizes human input to make subjective decisions on potential problem solutions. But humans are slow and get bored and tired easily, limiting the usefulness of IEAs. Here we describe our system which works toward overcoming these problems, The Approximate User (TAU), and also a simulated user as a means to test IEAs. With TAU, as the user interacts with the IEA a model of the user’s preferences is constructed and continually refined and this model is what is used as the fitness function to drive evolutionary search. The resulting system is a step toward our longer term goal of building a human-computer collaborative search system. In comparing the TAU IEA against a basic IEA it is found that TAU is 2.5 times faster and 15 times more reliable at producing near optimal results.