Believability of characters has been an objective in literature, theater, film, and animation. We argue that believable robot characters are important in human-robot interaction, as well. In particular, we contend that believable characters evoke users' social responses that, for some tasks, lead to more natural interactions and are associated with improved task performance. In a dialogue-capable robot, a key to such believability is the integration of a consistent storyline, verbal and nonverbal behaviors, and sociocultural context.

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).

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.

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.

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.