- J. Bongard, Derek P. Brock, S. Collins, R. Duraiswami, Timothy W. Finin, Ian Harrison, Vasant G Honavar, G. Hornby, A. Jónsson, Mike Kassoff, D. Kortenkamp, Sanjeev Kumar, Ken Murray, Alexander I. Rudnicky, G. Trajkovski. 2006. Aurally Informed Performance: Integrating Machine Listening and Auditory Presentation in Robotic Systems, Papers from the 2006 AAAI Fall Symposium, Washington, DC, USA, October 13-15, 2006. Abstract: This symposium brought together a number of researchers who are concerned with performance issues that robots face that depend, in some way, on sound. Many commercially marketed robotic platforms, as well as others that are moving from the laboratory into specialized public settings, already have rudimentary speech communication interfaces, and some are even being engineered for specific types of auditory tasks. In general, though, the ability of robots to monitor the auditory scene before them and to execute interactive behaviors informed by the interpretation or production of sound information remains far behind the broad and mostly transparent skills of human beings. It is an easy thing, for instance, for people to discern on the basis of audition alone who a familiar voice is, where the voice is located in the environment, and to act on other aspects of the au■ The American Association for Artificial Intelligence was pleased to present the AAAI 2006 Fall Symposium Series, held Friday through Sunday, October 13–15, at the Hyatt Regency Crystal City in Washington, DC. Seven symposia were held. The titles were (1) Aurally Informed Performance: Integrating Machine Listening and Auditory Presentation in Robotic Systems; (2) Capturing and Using Patterns for Evidence Detection; (3) Developmental Systems; (4) Integrating Reasoning into Everyday Applications; (5) Interaction and Emergent Phenomena in Societies of Agents; (6) Semantic Web for Collaborative Knowledge Acquisition; and (7) Spacecraft Autonomy: Using AI to Expand Human Space Exploration. ditory scene before them, such as noise, informative sounds, or the need for proximity or loudness to facilitate verbal communication. When these auditory skills are integrated with people’s other perceptual and reasoning abilities, substantial capacities for performance and interaction arise. The design goals for robot audition and utterance of information by sound, though, are not just those that correspond to human skills. Machine auditory sensing can be designed, in certain ways, to be more capable and acute than human hearing, and going beyond speech, robotic auditory displays can be engineered to render nonspeech auditory information in and for a variety of manners and purposes. Thus, a substantial interaction design space arises when different modes of human-robot interaction are augmented by conventional and enhanced auditory functions. Since the idea of “aurally informed performance” can be thought of as a two-sided proposition, involving both listening and presentation behaviors, the symposium was organized to focus on these themes separately and then conclude with a session on integrated systems. Much of the contributed research fit easily into this division. On each of the two main days, we began with an outline of recent research trends in the day’s topic, “Listening” on the first day and “Presentation” on the second, and then heard contributed talks from participants. Afternoons were devoted to critical discussions of talks given in the morning and examination of a relevant philosophical question about the nature and role of sound as information in the design context of robotics. The outline of trends in machine listening covered developments in sound localization and techniques for recognizing and extracting information from sound. Papers given under this day’s theme made several contributions in these areas. Novel methods for classifying acoustical environments, localizing sounds with headrelated transfer functions, organizing sounds with similar meanings, and perceiving and synthesizing speech were presented, and a chip being engineered for real-time classification of Reports on the 2006 AAAI Fall Symposia
