Enhancing the explanatory power of intelligent, model-based interfaces

This thesis considers intelligent, model-based systems and the system design choices that will result in enhanced explanatory power (i.e., more transparency and flexibility in the system). Intelligent systems provide advice to the end-user to assist in decision-making and problem-solving and model-based interfaces make use of a graphical representation of a model, which an end-user is free to interact with and explore to better understand the system's advice. Having a model-based interface is believed to facilitate the development of an appropriate mental model of the system, so that a user can become a more proficient problem-solver when interacting with an intelligent system. A primary objective of this dissertation is to offer empirically-based guidelines on how specific enhancements to explanatory power will improve system effectiveness. Two separate experiments were conducted to explore explanatory power by manipulating its three components: 1) content-based enhancements, 2) interface-based enhancements, and 3) the selection of an appropriate advisory strategy. Experiment I explored whether providing end-users with graphical-based hierarchies representing the structure of expert system rule-bases (interface-based enhancement) as well as with deep explanations, or underlying domain principles explaining system actions (content-based enhancement), actually improved problem-solving ability. Experiment II was an investigation that demonstrated how manipulations of advisory strategy affected explanatory power. For this experiment, two versions of an intelligent system were created: a high-restrictive system, in which the system prescribed for the end-user the order and manner in which the procedures of an intelligent system were to be used; and a low-restrictive system, in which the end-user was free to use an intelligent system's procedures in any sequence and in any manner he/she chose. Multiple methods of measurements were employed to understand the effects of different treatments in the study. Problem-solving performance, as assessed by a series of tasks, and execution time were the main dependent variables of interest. Questionnaires and essay questions were administered to all subjects to gain a deeper understanding of user preferences. Finally, to gain a richer understanding of the problem-solving process, all of the subjects' actions were captured in a computer log, so that problem-solving strategies could be reconstructed and analyzed.Business, Sauder School ofGraduat