This article describes research to build an embodied conversational agent as an interface to a question-and-answer system about a National Science Foundation program. We call this ECA the LifeLike Avatar, and it can interact with its users in spoken natural language to answer general as well as specific questions about specific topics. In an idealized case, the LifeLike Avatar could conceivably provide a user with a level of interaction such that he or she would not be certain as to whether (...) he or she is talking to the actual person via video teleconference. This could be considered a extended version of the seminal Turing test. Although passing such a test is still far off, our work moves the science in that direction. The Uncanny Valley notwithstanding, applications of such lifelike interfaces could include those where specific instructors/caregivers could be represented as stand-ins for the actual person in situations where personal representation is important. Possible areas that come to mind that might benefit from these lifelike ECAs include health-care support for elderly/disabled patients in extended home care, education/training, and knowledge preservation. Another more personal application would be to posthumously preserve elements of the persona of a loved one by family members. We apply this approach to a Q/a system for knowledge preservation and dissemination, where the specific individual who had this knowledge was to retire from the US National Science Foundation. The system is described in detail, and evaluations were performed to determine how well the system was perceived by users. (shrink)

This article describes and evaluates an approach to create and/or improve tactical agents through direct human interaction in real time through a force-feedback haptic device. This concept takes advantage of a force-feedback joystick to enhance motor skill and decision-making transfer from the human to the agent in real time. Haptic devices have been shown to have high bandwidth and sensitivity. Experiments are described for this new approach, named Instructional Learning. It is used both as a way to build agents from (...) scratch as well as to improve and/or correct agents built through other means. The approach is evaluated through experiments that involve three applications of increasing complexity – chasing a fleer, shepherding a flock of sheep into a pen, and driving a virtual automobile through a simulated road network. The results indicate that in some instances, instructional learning can successfully create agents under some circumstances. However, instructional learning failed to build and/or improve agents in other instances. The Instructional Learning approach, the experiments, and their results are described and extensively discussed. (shrink)