The Role of Partial Automation in Increasing the Accessibility of Digital Games

Digital games are designed to be controlled using hardware devices such as gamepads, keyboards, and cameras. Some device inputs may be inaccessible to players with motor impairments, rendering them unable to play. Games and devices can be adapted to enable play, but for some players these adaptations may not go far enough. Games may require inputs that some players cannot provide with any device. To address this problem, we introduce partial automation, an accessibility technique that delegates control of inaccessible game inputs to an AI partner. Partial automation complements and builds on other approaches to improving games' accessibility, including universal design, player balancing, and interface adaptation. We have demonstrated partial automation in two games for the rehabilitation of spinal cord injury. Six study participants with vastly different motor abilities were able to play both games. Participants liked the increased personalization that partial automation affords, although some participants were confused by aspects of the AI's behaviour.

Model-based intelligent user interface adaptation: challenges and future directions

Adapting the user interface of a software system to the requirements of the context of use continues to be a major challenge, particularly when users become more demanding in terms of adaptation quality. A considerable number of methods have, over the past three decades, provided some form of modelling with which to support user interface adaptation. There is, however, a crucial issue as regards in analysing the concepts, the underlying knowledge, and the user experience afforded by these methods as regards comparing their benefits and shortcomings. These methods are so numerous that positioning a new method in the state of the art is challenging. This paper, therefore, defines a conceptual reference framework for intelligent user interface adaptation containing a set of conceptual adaptation properties that are useful for model-based user interface adaptation. The objective of this set of properties is to understand any method, to compare various methods and to generate new ideas for adaptation. We also analyse the opportunities that machine learning techniques could provide for data processing and analysis in this context, and identify some open challenges in order to guarantee an appropriate user experience for end-users. The relevant literature and our experience in research and industrial collaboration have been used as the basis on which to propose future directions in which these challenges can be addressed.