Gaze and gestures are important modalities in human-human interactions and hence important to human-robot interaction. We describe how to use human gaze and robot pointing gestures to disambiguate and extend a human-robot speech dialogue developed for aiding people suffering from dementia.

Probabilistic graphical models, such as Bayesian networks, are intuitive and theoretically sound tools for modeling uncertainty. A major problem with applying Bayesian networks in practice is that it is hard to judge whether a model fits well a case that it is supposed to solve. One way of expressing a possible dissonance between a model and a case is the {\em surprise index}, proposed by Habbema, which expresses the degree of surprise by the evidence given the model. While this measure reflects the intuition that the probability of a case should be judged in the context of a model, it is computationally intractable. In this paper, we propose an efficient way of approximating the surprise index.

Just as there exists varied uses for computational models of narrative, there exists a wide variety of languages aimed at representing stories. A number of them have historic roots in automated generation, for which these languages have to be limited in order to make the generation process computationally feasible. Other are focused on story understanding, with close ties to natural language making many reasoning processes computationally intractable. In this paper, we discuss the trade-off between expressivity and computational complexity of the reasoning process and argue that Impulse, a temporal, modal logic provides more expressivity than languages historically associated with story generation, while still affording reasoning capabilities. We show that these properties enable certain aspects of narrative discourse generation by using two examples from different genres, and claim that this generalizes to a broader class of problems.

Socially Assistive Robotics (SAR) is a growing field dedicated to developing models and algorithms that enable robots to help people achieve goals through social interaction (Feil-Seifer and Mataric 2005). Prior work in this field has focused on one-on-one interactions, but there is interest in extending this work to multi-party interactions. We contribute to the study of multi-party SAR by defining the role of moderator, an agent that is responsible for directing an interaction, but is not necessarily directly participating in the task. We present a computational formalization of the task of moderation as the process by which a goal-directed multi-party interaction is regulated via manipulation of interaction resources, including both physical resources, such as an object or a tool, and social resources, such as the conversational floor or participants' attention. Finally, we present preliminary results of an analysis of self-moderated multi-party human-human interaction that support several of the underlying assumptions of this formalization.

Autonomous mobile service robots move in our buildings, carrying out different tasks and traversing multiple floors. While moving and performing their tasks, these robots find themselves in a variety of states. Although speech is often used for communicating the robot’s state to humans, such communication can often be ineffective, due to the transient nature of speech. In this paper, we investigate the use of lights as a persistent visualization of the robot’s state in relation to both tasks and environmental factors. Programmable lights offer a large degree of choices in terms of animation pattern, color and speed. We present this space of choices and introduce different animation profiles that we consider to animate a set of programmable lights on the robot. We conduct experiments to query about suitable animations for three representative scenarios of an autonomous symbiotic service robot, CoBot. Our work enables CoBot to make its states persistently visible to the humans it interacts with.

Natural Language Understanding (NLU) studies machine language comprehension and action without human intervention. We describe an implemented system that supports deep semantic NLU for controlling systems with multiple simulated robot agents. The system supports bidirectional communication for both human-agent and agent-agent inter-action. This interaction is achieved with the use of N-tuples, a novel form of Agent Communication Language using shared protocols with content expressing actions or intentions. The system’s portability and flexibility is facilitated by its division into unchanging “core” and “application-specific” components.

An integral part of social believability in role playing games is believability of non-player characters (NPC). In this paper we argue for the importance of believability in NPCs, even those that are completely outside of any pre-written quest or plot. We present NPCAgency, a system designed to generate many conversational NPCs as packaged narrative assets that can be shared and imported into various projects to increase story-world immersion. We believe such a system can help solve two problems. First, the authorial burden of the game designer is lessened, allowing renderings of large numbers of NPCs, each with their own unique background and conversation topics, all conforming to the norms of a predefined “universe”. Second, the immersive aspect of the game is heightened as the player can engage complex characters with lengthy dialogue affordances. We demonstrate the concept by generating fifty characters with attributes drawn from “Game of Thrones” (GOT) / “A Song of Ice and Fire” universe, and exporting them as Inform 7 code, a popular declarative interactive fiction (IF) programming language and authoring tool. A user study of thirty-seven Inform 7 programmers demonstrates that a 62% majority find the tool useful enough to consider for their own work. Further 70% said they would use the system to create “Game of Thrones” background characters for their own projects.

This work presents a general-purpose character reasoning model intended for usage by autonomous team actors that are acting as believable characters (e.g., human team actors fall into this category). The idea is that selecting a cast of believable characters can predetermine a solution to an unexpected challenge that the team may be facing in a rescue mission. This approach in certain cases proves more efficient than an alternative approach based on rational decision making and planning, which ignores the question of character believability. This point is illustrated with a simple numerical example in a virtual world paradigm.

The updating is accomplished using Bayes'

This paper presents an exploratory study where participants piloted a commercial UAS (unmanned aerial system) through an obstacle course. The goal was to determine how varying the instructions given to participants affected their performance. Preliminary data suggests future studies to perform, as well as guidelines for human-robot interaction, and some best practices for learning from demonstration studies.