For a given question q, the Regularized Information Gain is given as: where τ (q) is the expected information gain associated with asking a question and C(q) is the cost of asking a question q ∈ Q in a given game.
the information gain is measured as the KL divergence between the prior and posterior likelihoods of the scene objects before and after a certain question is made, weighted by a skewness coefficient β(q) over the same posterior.