In typical human interactions, nonverbal behaviors such as eye gazes and gestures serve to augment and reinforce spoken communication. To use similar nonverbal behaviors in human-robot interactions, researchers can apply artificial intelligence techniques such as machine learning, cognitive modeling, and computer vision. But knowledge of nonverbal behavior can also benefit artificial intelligence: because nonverbal communication can reveal human mental states, these behaviors provide additional input to artificial intelligence problems such as learning from demonstration, natural language processing, and motion planning. This article describes how nonverbal communication in HRI can benefit from AI techniques as well as how AI problems can use nonverbal communication in their solutions.

For humans and automation to effectively collaborate and perform tasks, all participants need access to a common representation of potentially relevant situational information, or context. This article describes a general framework for building context-aware interactive intelligent systems that comprises three major functions: (1) capture human-system interactions and infer implicit context; (2) analyze and predict user intent and goals; and (3) provide effective augmentation or mitigation strategies to improve performance, such as delivering timely, personalized information and recommendations, adjusting levels of automation, or adapting visualizations. We then describe our current work towards a general platform that supports developing context-aware applications in a variety of domains. We then explore an example use case illustrating how our framework can facilitate personalized collaboration within an information management and decision support tool.

For humans and automation to effectively collaborate and perform tasks, all participants need access to a common representation of potentially relevant situational information, or context. This article describes a general framework for building context-aware interactive intelligent systems that comprises three major functions: (1) capture human-system interactions and infer implicit context; (2) analyze and predict user intent and goals; and (3) provide effective augmentation or mitigation strategies to improve performance, such as delivering timely, personalized information and recommendations, adjusting levels of automation, or adapting visualizations. Our goal is to develop an approach that enables humans to interact with automation more intuitively and naturally that is reusable across domains by modeling context and algorithms at a higher-level of abstraction. We first provide an operational definition of context and discuss challenges and opportunities for exploiting context. We then describe our current work towards a general platform that supports developing context-aware applications in a variety of domains. We then explore an example use case illustrating how our framework can facilitate personalized collaboration within an information management and decision support tool. Future work includes evaluating our framework.

In this paper we assess our progress toward creating a virtual human negotiation agent with fluid turn-taking skills. To facilitate the design of this agent, we have collected a corpus of human-human negotiation roleplays as well as a corpus of Wizard-controlled human-agent negotiations in the same roleplay scenario. We compare the natural turn-taking behavior in our human-human corpus with that achieved in our Wizard-of-Oz corpus, and quantify our virtual human's turn-taking skills using a combination of subjective and objective metrics. We also discuss our design for a Wizard user interface to support real-time control of the virtual human's turn-taking and dialogue behavior, and analyze our wizard's usage of this interface.

We present the SimSensei system, a fully automatic virtual agent that conducts interviews to assess indicators of psychological distress. We emphasize on the perception part of the system, a multimodal framework which captures and analyzes user state for both behavioral understanding and interactional purposes.

We present an approach to incorporate interesting and compelling characters in planning-based narrative generation. The approach is based on a computational model that utilizes character actions to portray these as having distinct and well-defined personalities.

This paper presents an exploratory study conducted to understand how audio-visual prompts are understood by people on an emotional level as a first step towards the more challenging task of designing emotionally aligned prompts for persons with cognitive disabilities such as Alzheimer’s disease and related dementias (ADRD). Persons with ADRD often need assistance from a caregiver to complete daily living activities such as washing hands, making food, or getting dressed. Artificially intelligent systems have been developed that can assist in such situations. This paper presents a set of prompt videos of a virtual human ‘Rachel’, wherein she expressively communicates prompts at each step of a simple hand washing task, with various human-like emotions and behaviors. A user study was conducted for 30 such videos with respect to three basic and important dimensions of emotional experience: evaluation, potency, and activity. The results show that, while people generally agree on the evaluation (valence: good/bad) of a prompt, consensus about power and activity is not as socially homogeneous. Our long term aim is to enhance such systems by delivering automated prompts that are emotionally aligned with individuals in order to help with prompt adherence and with long-term adoption.

He then solves the simpler problem and uses the information obtained in the solution of the simpler problem in the solution of the original complex problem.

The editors have provided him with special introductory sections as a ing guide to a thorough understand of the reports.

Sequential learning for classification tasks is an effective tool in the machine learning community. In sequential learning settings, algorithms sometimes make incorrect predictions on data that were correctly classified in the past. This paper explicitly deals with such inconsistent prediction behavior. Our main contributions are 1) to experimentally show its effect for user utilities as a human cognitive bias, 2) to formalize a new framework by internalizing this bias into the optimization problem, 3) to develop new algorithms without memorization of the past prediction history, and 4) to show some theoretical guarantees of our derived algorithm for both online and stochastic learning settings. Our experimental results show the superiority of the derived algorithm for problems involving human cognition.