Learning how to trade off aesthetic criteria in layout

Typesetting software is often faced with conflicting aesthetic goals. For example, choosing where to break lines in text might involve aiming to minimize hyphenation, variation in word spacing, and consecutive lines starting with the same word. Typically, automatic layout is modelled as an optimization problem in which the goal is to minimize a complex objective function that combines various penalty functions each of which corresponds to a particular bad feature. Determining how to combine these penalty functions is difficult and very time consuming, becoming harder each time we add another penalty. Here we present a machine-learning approach to do this, and test it in the context of line-breaking. Our approach repeatedly queries the expert typographer as to which one of a pair of layouts is better, and accordingly refines the estimate of how best to weight the penalties in a linear combination. It chooses layout pair queries by a heuristic to maximize the amount that can be learnt from them so as to reduce the number of combinations that must be considered by the typographer.