Accelerating Human-Computer Collaborative Search through Learning Comparative and Predictive User Models
CALIFORNIA UNIV SANTA CRUZ
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Interactive Evolutionary Algorithms IEAs are one of the few systems in which a human user and a computer algorithm are collaboratively working on a problem. To turn a basic IEA into the start of a Human-Computer Collaborative Computational system we have developed a system called The Approximate User TAU. With TAU, as the user interacts with the IEA a model of the users preferences is constructed and continually refined and it is this user-model which drives search. Here 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.
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