Different interfaces allow a user to achieve the same end goal through different action sequences, e.g., command lines vs. drop down menus. Interface efficiency can be described in terms of a cost incurred, e.g., time taken, by the user in typical tasks. Realistic users arrive at evaluations of efficiency, hence making choices about which interface to use, over time, based on trial and error experience. Their choices are also determined by prior experience, which determines how much learning time is required. These factors have substantial effect on the adoption of new interfaces. In this paper, we aim at understanding how users adapt under interface change, how much time it takes them to learn to interact optimally with an interface, and how this learning could be expedited through intermediate interfaces. We present results from a series of experiments that make four main points: (a) different interfaces for accomplishing the same task can elicit significant variability in performance, (b) switching interfaces can result in adverse sharp shifts in performance, (c) subject to some variability, there are individual thresholds on tolerance to this kind of performance degradation with an interface, causing users to potentially abandon what may be a pretty good interface, and (d) our main result -- shaping user learning through the presentation of intermediate interfaces can mitigate the adverse shifts in performance while still enabling the eventual improved performance with the complex interface upon the user becoming suitably accustomed. In our experiments, human users use keyboard based interfaces to navigate a simulated ball through a maze. Our results are a first step towards interface adaptation algorithms that architect choice to accommodate personality traits of realistic users.

In transfer learning we aim to solve new problems using fewer examples using information gained from solving related problems. Transfer learning has been successful in practice, and extensive PAC analysis of these methods has been de- veloped. However it is not yet clear how to define relatedness between tasks. This is considered as a major problem as it is conceptually troubling and it makes it unclear how much information to transfer and when and how to transfer it. In this paper we propose to measure the amount of information one task contains about another using conditional Kolmogorov complexity between the tasks. We show how existing theory neatly solves the problem of measuring relatedness and transferring the ‘right’ amount of information in sequential transfer learning in a Bayesian setting. The theory also suggests that, in a very formal and precise sense, no other reasonable transfer method can do much better than our Kolmogorov Complexity theoretic transfer method, and that sequential transfer is always justi- fied. We also develop a practical approximation to the method and use it to transfer information between 8 arbitrarily chosen databases from the UCI ML repository.