Modelling Human Active Search in Optimizing Black-box Functions
This work provides insights into human cognitive processes for black-box optimization, but it is incremental as it applies existing methods to a new comparison scenario.
The paper tackled the problem of comparing human active search behavior to Bayesian Optimization models when maximizing an unknown 2D function, finding through experiments with 60 subjects that Bayesian Optimization effectively models human active learning patterns.
Modelling human function learning has been the subject of in-tense research in cognitive sciences. The topic is relevant in black-box optimization where information about the objective and/or constraints is not available and must be learned through function evaluations. In this paper we focus on the relation between the behaviour of humans searching for the maximum and the probabilistic model used in Bayesian Optimization. As surrogate models of the unknown function both Gaussian Processes and Random Forest have been considered: the Bayesian learning paradigm is central in the development of active learning approaches balancing exploration/exploitation in uncertain conditions towards effective generalization in large decision spaces. In this paper we analyse experimentally how Bayesian Optimization compares to humans searching for the maximum of an unknown 2D function. A set of controlled experiments with 60 subjects, using both surrogate models, confirm that Bayesian Optimization provides a general model to represent individual patterns of active learning in humans