n order for robots to engage in dialog with human teammates, they must have the ability to map between words in the language and aspects of the external world. A solution to this symbol grounding problem (Harnad, 1990) would enable a robot to interpret commands such as “Drive over to receiving and pick up the tire pallet.” In this article we describe several of our results that use probabilistic inference to address the symbol grounding problem. Our specific approach is to develop models that factor according to the linguistic structure of a command. We first describe an early result, a generative model that factors according to the sequential structure of language, and then discuss our new framework, generalized grounding graphs (G3). The G3 framework dynamically instantiates a probabilistic graphical model for a natural language input, enabling a mapping between words in language and concrete objects, places, paths and events in the external world. We report on corpus-based experiments where the robot is able to learn and use word meanings in three real-world tasks: indoor navigation, spatial language video retrieval, and mobile manipulation.

Turn-taking is a fundamental part of human communication. Our goal is to devise a turn-taking framework for human-robot interaction that, like the human skill, represents something fundamental about interaction, generic to context or domain. We propose a model of turn-taking, and conduct an experiment with human subjects to inform this model. Our findings from this study suggest that information flow is an integral part of human floor-passing behavior. Following this, we implement autonomous floor relinquishing on a robot and discuss our insights into the nature of a general turn-taking model for human-robot interaction.

Winter news from the Association for Advancement of Artificial Intelligence. Includes the Independent Auditor's Report.

This page includes forthcoming AAAI sponsored conferences, conferences presented ICEIS 2012 will be held June 28 by AAAI Affiliates, and conferences held in cooperation with AAAI. RuleML-2012 will be AAAI Spring Symposium Series. The Thirteenth International Conference held August 27-31, 2012 in Montpellier, AAAI Spring Symposium Series will be on Principles of Knowledge France. KR The Third International Conference University, Stanford, California, USA 2012 will be held June 10-14, 2012 in on Computational Creativity. ICWSM-12 will be held June 4-7 at ICAPS 2012 will be held June 24-28, Twenty-Fifth International Conference Trinity College in Dublin, Ireland.

The 7th International Conference on Intelligent Environments (IE11) was held July 25–28 2011 at the Nottingham Trent University, Nottingham, UK. The general chairs were Ahmad Lotfi (Nottingham Trent University), and Sean Hanna (Bartlett School of Graduate Studies, University College London). Juan Carlos Augusto (University of Ulster) and Achilles Kameas (Hellenic Open University and Computer Technology Institute), served as program chairs. This article presents a report of the conference.

Modeling interaction with robots raises new and different challenges for dialog modeling than traditional dialog modeling with less embodied machines. We present four case studies of implementing a typical human-robot interaction scenario with different state-of-the-art dialog frameworks in order to identify challenges and pitfalls specific to HRI and potential solutions. The results are discussed with a special focus on the interplay between dialog and task modeling on robots.

Many human social exchanges and coordinated activities critically involve dialogue interactions. Hence, we need to develop natural humanlike dialogue processing mechanisms for future robots if they are to interact with humans in natural ways. In this article we discuss the challenges of designing such flexible dialogue-based robotic systems. We report results from data we collected in human interaction experiments in the context of a search task and show how we can use these results to build more flexible robotic architectures that are starting to address the challenges of task-based humanlike natural language dialogues on robots.

While such systems have been shown to successfully support a broad range of interactions, they rely heavily on precoded data. For example, dialogue responses are typically limited to only one or two dozen phrases, which pales in comparison to the diversity of human speech. We believe that in order for robotic systems to become a truly ubiquitous technology, robots must make sense of natural human behavior and engage with humans in a more humanlike way. Robots must become more like humans instead of forcing humans to be more like robots. Much of human knowledge about the appropriateness of behavior, in terms of both speech and actions, comes from our personal experiences and our observations of others. We compare its performance variations form a knowledge base from which to a teleoperated robot following a scripted task we learn what to say and what actions to perform to protocol and examine both the behavior of the achieve certain goals.

If human-robot interaction is mainly shaped by users’ strategies to deal with their unfamiliar artificial com¬munication partner, as it is suggested here, robot dialog design should orient at reducing users’ uncertainty about the affordances of the robot and the joint task. Two experiments are presented that investigate the impact of verbal robot utterances on users’ behavior; results show that users react sensitively to subtle linguistic cues that may guide them into appropriate understandings of the robot. Furthermore, the role of user expectations and robot appearance are discussed in the light of the model presented.

Of all computational systems, robots are unique in their ability to afford embodied interaction using the wider range of human communicative cues. Research on human communication provides strong evidence that embodied cues, when used effectively, elicit social, cognitive, and task outcomes such as improved learning, rapport, motivation, persuasion, and collaborative task performance. While this connection between embodied cues and key outcomes provides a unique opportunity for design, taking advantage of it requires a deeper understanding of how robots might use these cues effectively and the limitations in the extent to which they might achieve such outcomes through embodied interaction. This article aims to underline this opportunity by providing an overview of key embodied cues and outcomes in human communication and describing a research program that explores how robots might generate high-level social, cognitive, and task outcomes such as learning, rapport, and persuasion using embodied cues such as verbal, vocal, and nonverbal cues.