The science and application of HCI continues to evolve with more practitioners, scientists, researchers and developers seek to further what it means to human society and how it can be leveraged to address social and economic issues as well as to determine how people can think and work smarter. It's become such a relevant area of study that university programs and degrees are now available for HCI as a way of furthering the understanding and application for this segment of computer science. At the same time, new jobs are emerging to use these degrees related to furthering areas like those being studied by IBM or as new companies develop applications for artificial intelligence and connected devices that bring us further into the world of computers.
We describe an implemented multimodal travel guide application being employed in a set of Wizard of Oz experiments from which data about user interactions is gathered. We offer a preliminary analysis of the data which suggests that, as is evident in Huls et al.'s (1995) more extensive study, the interpretation of referring expressions can be accounted for by a rather simple set of rules which do not make reference to the type of referring expression used. As this result is perhaps unexpected in light of past linguistic research on reference, we suspect that this is not a general result, but instead a product of the simplicity of the tasks around which these multimodal systems have been developed. Thus, more complex systems capable of evoking richer sets of human language and gestural communication need to be developed before conclusions can be drawn about unified representations for salience and reference in multimodal settings.
DIARC, a distributed integrated affect, reflection, cognition architecture for robots, provides many features that are critical to successful natural human-robot interaction. As such, DIARC is an ideal platform for experimentation in HRI. In this paper we describe the architecture and and its implementation in ADE, paying particular attention to its interaction capabilities and features that allow robust operation. These features are evaluated in the context of the 2006 AAAI Robot Competition.
Principles for human-AI interaction have been discussed in the human-computer interaction community for over two decades, but more study and innovation are needed in light of advances in AI and the growing uses of AI technologies in human-facing applications. We propose 18 generally applicable design guidelines for human-AI interaction. These guidelines are validated through multiple rounds of evaluation including a user study with 49 design practitioners who tested the guidelines against 20 popular AI-infused products. The results verify the relevance of the guidelines over a spectrum of interaction scenarios and reveal gaps in our knowledge, highlighting opportunities for further research. Based on the evaluations, we believe the set of design guidelines can serve as a resource to practitioners working on the design of applications and features that harness AI technologies, and to researchers interested in the further development of guidelines for human-AI interaction design.