Introduction: ML meets HCI

Abstract

This special issue is devoted to invited articles on machine learning (ML). Most of the articles included in this issue were also presented in a special session on ML at the 8th International Conference on Human-Compute r Interaction that was held at Yokohama, Japan in July 1995 (Anzai, Ogawa, & Mori, 1995). Since the publication of the first volume of Machine Learning: An Artificial Intelligence Approach (Michalski, Carbonell, & Mitchell, 1983), ML has progressed significantly and several applications have been reported, whereas several others have remained unpublished. In the same volume, the Nobel prize winner Herbert A. Simon places ML in context with learning by stating that learning denotes changes in the system that are adaptive in the sense that they enable the system to do the same task or tasks drawn from the same population more efficiently and more effectively the next time. Many scientific journals and international conferences have hosted special sections and sessions reporting on ML or on applications of ML. Knowledge acquisition, planning, scheduling, decision support, transportation, medicine, and engineering, among others, compose the domains in which ML has been both applied, proved effective, and continues to do so. An attempt to review all ML applications or theory developments would render this introduction or even the special issue endless. In part, a goal of this issue is to extend hands between the two communities: human-computer interaction (HCI) and ML. To a large degree, both share a common goal: Each one tries to improve the human performance and adaptability to changing conditions of some system. Enhancing systems with learning ability may prove conducive to building better systems. Humans come in life with built-in learning potential and excluding artifacts from learning may seriously impede user acceptability of new technology. The article by Moustakis, Lehto, and Salvendy captures expert judgment about a critical question: Which ML method should be used for a given task? The article is based on an extensive survey of ML experts and statistical analysis of responses. It also kicks off the special issue because it briefly introduces the reader to the various ML methods and tasks in which ML may be used. The article by Yoshida and Motoda presents a framework for using ML to automate user modeling and behavior in a user adaptive interface system. It uses