The legal relationship between government and citizens is mediated by documents. This paper identifies four classes of cognitive assistants that could improve the experience of citizens and government officials in using and understanding government documents: self-filling forms; error-detecting forms; proactive information search; and deductive document synthesis. Each of these classes of cognitive assistants has the potential to significantly improve access to justice and delivery of information, services, and other benefits to citizens by improving the ability of citizens to understand and correctly fill out forms and to comprehend informational documents.

This abstract is proposing a challenging problem: to infer a human's mental state — intent and belief — from an observed RGBD video for human-robot interaction. The task is to integrate symbolic reasoning, a field well-studied within A.I. domains, with the uncertainty native to computer vision strategies. Traditional A.I. strategies for plan inference typically rely on first-order logic and closed world assumptions which struggle to take into account the inherent uncertainty of noisy observations within a scene. Computer vision relies on pattern-recognition strategies that have difficulty accounting for higher-level reasoning and abstract representation of world knowledge. By combining these two approaches in a principled way under a probabilistic programming framework, we define new computer vision tasks such as actor intent prediction and belief inference from an observed video sequence. Through inferring a human's theory of mind, a robotic agent can automatically determine a human's goals to collaborate with them.

Natural language dialogue provides the opportunity for truly natural human-robot interaction. A robot participating in natural language dialogue must identify or create new representations for referenced entities if it is to discuss, reason about, or perform actions involving that entity, a capability known as reference resolution. In previous work we presented algorithms for resolving references occurring in definite noun phrases. In this paper we propose an algorithm for resolving references in a wider array of linguistic forms, using the Givenness Hierarchy.

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.

Issuing and following instructions is a common task in many forms of both human-human and human-robot collaboration. With two human participants, the accuracy of instruction following increases if the collaborators can monitor the state of their partners and respond to them through conversation (Clark and Krych 2004), a process we call social feedback. Despite this benefit in human-human interaction, current human-robot collaboration systems process instructions in non-incremental batches, which can achieve good accuracy but does not allow for reactive feedback (Tellex et al. 2011; Matuszek et al. 2012; Tellex et al. 2012; Misra et al.2014). In this paper, we show that giving a robot the ability to ask the user questions results in responsive conversations and allows the robot to quickly determine the object that the user desires. This social feedback loop between person and robot allows a person to create an internal model for the robot’s mental state and adapt their own behavior to better inform the robot. To close the human-robot feedback loop, we employ a Partially Observable Markov Decision Process (POMDP) to produce a policy which will lead to the determination of the object in the shortest amount of time. To test our approach, we perform user studies to measure our robot’s ability to deliver common household items requested by the participant. We compare delivery speed and accuracy both with and without social feedback.

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.

We report a study performed with a social robot that autonomously plays a competitive game. By relying on an emotional agent architecture (using an appraisal mechanism) the robot was built with the capabilities of emotional appraisal and thus was able to express and share its emotions verbally throughout the game. Contrary to what was expected, emotional sharing in this context seemed to damage the social interaction with the users.

How can robots fluently communicate with humans and have context-preserving conversation? It is the most momentous and crucial problem in robotics research, especially for service robots such as child-care robots. Here, we aim to develop a situated conversation system for child-care robots. The conversation system considers the current context between robots and children as well as the situation the child is in. The system consists of two parts. The first part tries to understand the context. This part uses the embedded sensors of robots to understand the context and wearable sensors of the child for getting information of the situation the child is in. The second part is to generate the situated conversation. In terms of the model, we designed a hierarchical Bayesian Network for the first part and a Hypernetwork model is used for the second part. We illustrate the application of communication with a child in a child-care service robots scenario. For this application, we collect wearable sensors’ data from the child and mother-child conversation data in daily life. Finally, we discuss our results and future works.

An ongoing goal at the intersection of artificial intelligence In this paper, we briefly present initial work that has (AI), robotics, and human-robot interaction (HRI) is to create been done in the DIARC/ADE cognitive robotic architecture autonomous agents that can assist and interact with human (Schermerhorn et al. 2006; Kramer and Scheutz 2006) to enable teammates in natural and humanlike ways. This is a such a rejection and explanation mechanism. First we multifaceted challenge, involving both the development of discuss the theoretical considerations behind this challenge, an ever-expanding set of capabilities (both physical and algorithmic) specifically the conditions that must be met for a directive to such that robotic agents can autonomously engage be appropriately accepted. Next, we briefly present some of in a variety of useful tasks, as well as the development the explicit reasoning mechanisms developed in order to facilitate of interaction mechanisms (e.g.

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.