AR-Mentor is a wearable real time Augmented Reality (AR) mentoring system that is configured to assist in maintenance and repair tasks of complex machinery, such as vehicles, appliances, and industrial machinery. The system combines a wearable Optical-See-Through (OST) display device with high precision 6-Degree-Of-Freedom (DOF) pose tracking and a virtual personal assistant (VPA) with natural language, verbal conversational interaction, providing guidance to the user in the form of visual, audio and locational cues. The system is designed to be heads-up and hands-free allowing the user to freely move about the maintenance or training environment and receive globally aligned and context aware visual and audio instructions (animations, symbolic icons, text, multimedia content, speech). The user can interact with the system, ask questions and get clarifications and specific guidance for the task at hand. A pilot application with AR-Mentor was successfully built to instruct a novice to perform an advanced 33-step maintenance task on a training vehicle. The initial live training tests demonstrate that AR-Mentor is able to help and serve as an assistant to an instructor, freeing him/her to cover more students and to focus on higher-order teaching.

As adaptive agents become more complex and take increasing autonomy in their user's lives, it becomes more important for users to trust and understand these agents. Little work has been done, however, to study what factors influence the level of trust users are willing to place in these agents. Without trust in the actions and results produced by these agents, their use and adoption as trusted assistants and partners will be severely limited. We present the results of a study among test users of CALO, one such complex adaptive agent system, to investigate themes surrounding trust and understandability. We identify and discuss eight major themes that significantly impact user trust in complex systems. We further provide guidelines for the design of trustable adaptive agents. Based on our analysis of these results, we conclude that the availability of explanation capabilities in these agents can address the majority of trust concerns identified by users.

Effectively distributing information to members of a large organization poses a number of challenges. Ideal information distribution within an organization will be timely, selective, and (to some degree) automatic. It is difficult to meet all three of these criteria simultaneously using existing techniques in information retrieval and information filtering. This paper introduces a new method of distributing information automatically, based on enterprise models --- representations of the pertinent aspects of an organization's structure and operation. The approach is based on the premise that often information needs within an organization are implicitly represented by paths through the enterprise model. These paths are discovered in our approach through the use of Generalized Path Models (GPMs). A GPM specifies (1) the conditions that instigate a path search when new information is created, (2) a general class of paths for which to search, and (3) an action to take when a suitable path is f...