Principal–agent learning

In this paper we present a merging, and hence an extension, of two recent learning methods, utility-based learning and strategic or adversarial learning. Recently, utility-based learning brings to the forefront the learner's utility function during induction. Strategic learning anticipates strategic activity in the induction process when the instances are intelligent agents such as in classification problems involving people or organizations. We call the resulting merged model principal–agent learning and present an induction process and example. Our model collapses to utility-based models when the agents do not engage in strategic behavior and to strategic learning when the learner's utility is not considered.