Publications

@inproceedings{alavi_which_2020,

title = {Which Model Should We Use for a Real-World Conversational Dialogue System? a Cross-Language Relevance Model or a Deep Neural Net?},

author = {Seyed Hossein Alavi and Anton Leuski and David Traum},

url = {https://www.aclweb.org/anthology/2020.lrec-1.92/},

year  = {2020},

date = {2020-05-01},

booktitle = {Proceedings of the 12th Language Resources and Evaluation Conference},

pages = {735--742},

publisher = {European Language Resources Association},

address = {Marseille, France},

abstract = {We compare two models for corpus-based selection of dialogue responses: one based on cross-language relevance with a cross-language LSTM model. Each model is tested on multiple corpora, collected from two different types of dialogue source material. Results show that while the LSTM model performs adequately on a very large corpus (millions of utterances), its performance is dominated by the cross-language relevance model for a more moderate-sized corpus (ten thousands of utterances).},

keywords = {ARO-Coop, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lei_emotion_2020,

title = {Emotion or expressivity? An automated analysis of nonverbal perception in a social dilemma},

author = {Su Lei and Kalin Stefanov and Jonathan Gratch},

url = {https://www.computer.org/csdl/proceedings-article/fg/2020/307900a770/1kecIWT5wmA},

doi = {10.1109/FG47880.2020.00123},

year  = {2020},

date = {2020-05-01},

booktitle = {Proceedings of the 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) (FG)},

pages = {8},

publisher = {IEEE},

address = {Buenos Aires, Argentina},

abstract = {An extensive body of research has examined how specific emotional expressions shape social perceptions and social decisions, yet recent scholarship in emotion research has raised questions about the validity of emotion as a construct. In this article, we contrast the value of measuring emotional expressions with the more general construct of expressivity (in the sense of conveying a thought or emotion through any nonverbal behavior) and develop models that can automatically extract perceived expressivity from videos. Although less extensive, a solid body of research has shown expressivity to be an important element when studying interpersonal perception, particularly in psychiatric contexts. Here we examine the role expressivity plays in predicting social perceptions and decisions in the context of a social dilemma. We show that perceivers use more than facial expressions when making judgments of expressivity and see these expressions as conveying thoughts as well as emotions (although facial expressions and emotional attributions explain most of the variance in these judgments). We next show that expressivity can be predicted with high accuracy using Lasso and random forests. Our analysis shows that features related to motion dynamics are particularly important for modeling these judgments. We also show that learned models of expressivity have value in recognizing important aspects of a social situation. First, we revisit a previously published finding which showed that smile intensity was associated with the unexpectedness of outcomes in social dilemmas; instead, we show that expressivity is a better predictor (and explanation) of this finding. Second, we provide preliminary evidence that expressivity is useful for identifying “moments of interest” in a video sequence.},

keywords = {ARO-Coop, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{georgila_predicting_2020,

title = {Predicting Ratings of Real Dialogue Participants from Artificial Data and Ratings of Human Dialogue Observers},

author = {Kallirroi Georgila and Carla Gordon and Volodymyr Yanov and David Traum},

url = {https://www.aclweb.org/anthology/2020.lrec-1.91/},

year  = {2020},

date = {2020-05-01},

booktitle = {Proceedings of the 12th Language Resources and Evaluation Conference},

pages = {726--734},

publisher = {European Language Resources Association},

address = {Marseille, France},

abstract = {We collected a corpus of dialogues in a Wizard of Oz (WOz) setting in the Internet of Things (IoT) domain. We asked users participating in these dialogues to rate the system on a number of aspects, namely, intelligence, naturalness, personality, friendliness, their enjoyment, overall quality, and whether they would recommend the system to others. Then we asked dialogue observers, i.e., Amazon Mechanical Turkers (MTurkers), to rate these dialogues on the same aspects. We also generated simulated dialogues between dialogue policies and simulated users and asked MTurkers to rate them again on the same aspects. Using linear regression, we developed dialogue evaluation functions based on features from the simulated dialogues and the MTurkers’ ratings, the WOz dialogues and the MTurkers’ ratings, and the WOz dialogues and the WOz participants’ ratings. We applied all these dialogue evaluation functions to a held-out portion of our WOz dialogues, and we report results on the predictive power of these different types of dialogue evaluation functions. Our results suggest that for three conversational aspects (intelligence, naturalness, overall quality) just training evaluation functions on simulated data could be sufficient.},

keywords = {ARO-Coop, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{mozgai_iterative_2020,

title = {Iterative Participatory Design for VRET Domain Transfer: From Combat Exposure to Military Sexual Trauma},

author = {Sharon Mozgai and Arno Hartholt and Andrew Leeds and Albert ‘Skip’ Rizzo},

url = {https://dl.acm.org/doi/abs/10.1145/3334480.3375219},

doi = {10.1145/3334480.3375219},

year  = {2020},

date = {2020-04-01},

booktitle = {Proceedings of the 2020 CHI Conference of Human Factors in Computing Systems},

pages = {8},

publisher = {ACM},

address = {Honolulu, HI},

abstract = {This case study describes the expansion of the BRAVEMIND virtual reality exposure therapy (VRET) system from the domain of combat-related posttraumatic stress disorder (PTSD) to the domain of PTSD due to Military Sexual Trauma (MST). As VRET continues to demonstrate efficacy in treating PTSD across multiple trauma types and anxiety disorders, adapting existing systems and content to new domains while simultaneously maintaining clinical integrity is becoming a high priority. To develop BRAVEMIND-MST we engaged in an iterative participatory design process with psychologists, engineers, and artists. This first-person account of our collaborative development process focuses on three key areas (1) VR Environment, (2) User-Avatar State, and (3) Events, while detailing the challenges we encountered and lessons learned. This process culminated in eight design guidelines as a first-step in defining a VRET domain transfer methodology.},

keywords = {MedVR, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{chaffey_human_2020,

title = {Human swarm interaction using plays, audibles, and a virtual spokesperson},

author = {Patricia Chaffey and Ron Artstein and Kallirroi Georgila and Kimberly A. Pollard and Setareh Nasihati Gilani and David M. Krum and David Nelson and Kevin Huynh and Alesia Gainer and Seyed Hossein Alavi and Rhys Yahata and Anton Leuski and Volodymyr Yanov and David Traum},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11413/2557573/Human-swarm-interaction-using-plays-audibles-and-a-virtual-spokesperson/10.1117/12.2557573.full},

doi = {10.1117/12.2557573},

isbn = {978-1-5106-3603-3 978-1-5106-3604-0},

year  = {2020},

date = {2020-04-01},

booktitle = {Proceedings of Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II},

pages = {40},

publisher = {SPIE},

address = {Online Only, United States},

abstract = {This study explores two hypotheses about human-agent teaming: 1. Real-time coordination among a large set of autonomous robots can be achieved using predefined “plays” which define how to execute a task, and “audibles” which modify the play on the fly; 2. A spokesperson agent can serve as a representative for a group of robots, relaying information between the robots and human teammates. These hypotheses are tested in a simulated game environment: a human participant leads a search-and-rescue operation to evacuate a town threatened by an approaching wildfire, with the object of saving as many lives as possible. The participant communicates verbally with a virtual agent controlling a team of ten aerial robots and one ground vehicle, while observing a live map display with real-time location of the fire and identified survivors. Since full automation is not currently possible, two human controllers control the agent’s speech and actions, and input parameters to the robots, which then operate autonomously until the parameters are changed. Designated plays include monitoring the spread of fire, searching for survivors, broadcasting warnings, guiding residents to safety, and sending the rescue vehicle. A successful evacuation of all the residents requires personal intervention in some cases (e.g., stubborn residents) while delegating other responsibilities to the spokesperson agent and robots, all in a rapidly changing scene. The study records the participants’ verbal and nonverbal behavior in order to identify strategies people use when communicating with robotic swarms, and to collect data for eventual automation.},

keywords = {ARL, DoD, MxR, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bellas_rapport_2020,

title = {Rapport Building with Social Robots as a Method for Improving Mission Debriefing in Human-Robot Teams},

author = {Alexandria Bellas and Stefawn Perrin and Brandon Malone and Kaytlin Rogers and Gale Lucas and Elizabeth Phillips and Chad Tossell and Ewart de Visser},

url = {https://ieeexplore.ieee.org/document/9106643/},

doi = {10.1109/SIEDS49339.2020.9106643},

isbn = {978-1-72817-145-6},

year  = {2020},

date = {2020-04-01},

booktitle = {Proceedings of the 2020 Systems and Information Engineering Design Symposium (SIEDS)},

pages = {160--163},

publisher = {IEEE},

address = {Charlottesville, VA, USA},

abstract = {Conflicts may arise at any time during military debriefing meetings, especially in high intensity deployed settings. When such conflicts arise, it takes time to get everyone back into a receptive state of mind so that they engage in reflective discussion rather than unproductive arguing. It has been proposed by some that the use of social robots equipped with social abilities such as emotion regulation through rapport building may help to deescalate these situations to facilitate critical operational decisions. However, in military settings, the same AI agent used in the pre-brief of a mission may not be the same one used in the debrief. The purpose of this study was to determine whether a brief rapport-building session with a social robot could create a connection between a human and a robot agent, and whether consistency in the embodiment of the robot agent was necessary for maintaining this connection once formed. We report the results of a pilot study conducted at the United States Air Force Academy which simulated a military mission (i.e., Gravity and Strike). Participants’ connection with the agent, sense of trust, and overall likeability revealed that early rapport building can be beneficial for military missions.},

keywords = {ARO-Coop, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{mozgai_adaptive_2020,

title = {An Adaptive Agent-Based Interface for Personalized Health Interventions},

author = {Sharon Mozgai and Arno Hartholt and Albert "Skip" Rizzo},

url = {https://dl.acm.org/doi/10.1145/3379336.3381467},

doi = {10.1145/3379336.3381467},

isbn = {978-1-4503-7513-9},

year  = {2020},

date = {2020-03-01},

booktitle = {Proceedings of the 25th International Conference on Intelligent User Interfaces Companion},

pages = {118--119},

publisher = {ACM},

address = {Cagliari Italy},

abstract = {This demo introduces a novel mHealth application with an agentbased interface designed to collect multimodal data with passive sensors native to popular wearables (e.g., Apple Watch, FitBit, and Garmin) as well as through user self-report. This mHealth application delivers personalized and adaptive multimedia content via smartphone application specifically tailored to the user in the interdependent domains of physical, cognitive, and emotional health via novel adaptive logic-based algorithms while employing behavior change techniques (e.g., goal-setting, barrier identification, etc.). A virtual human coach leads all interactions to improve adherence.},

keywords = {MedVR, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{mozgai_passive_2020,

title = {The Passive Sensing Agent: A Multimodal Adaptive mHealth Application},

author = {Sharon Mozgai and Arno Hartholt and Albert Rizzo},

url = {https://ieeexplore.ieee.org/document/9156177/},

doi = {10.1109/PerComWorkshops48775.2020.9156177},

isbn = {978-1-72814-716-1},

year  = {2020},

date = {2020-03-01},

booktitle = {Proceedings of the 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)},

pages = {1--3},

publisher = {IEEE},

address = {Austin, TX, USA},

abstract = {We are demoing the Passive Sensing Agent (PSA), an mHealth virtual human coach, that collects multimodal data through passive sensors native to popular wearables (e.g., Apple Watch, FitBit, and Garmin). This virtual human interface delivers adaptive multi-media content via smartphone application that is specifically tailored to the user in the interdependent domains of physical, cognitive, and emotional health. Initially developed for the military, the PSA delivers health interventions (e.g., educational exercises, physical challenges, and performance feedback) matched to the individual user via novel adaptive logic-based algorithms while employing various behavior change techniques (e.g., goal-setting, barrier identification, rewards, modeling, etc.). A virtual human coach leads all interactions including the firsttime user experience and the brief daily sessions. All interactions were specifically designed to engage and motivate the user while continuously collecting data on their cognitive, emotional, and physical fitness. This multi-component application is integrated and deployed on an iPhone and Apple Watch prototype; a civilian version is currently in-development.},

keywords = {MedVR, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rosenbloom_subsymbolic_2019,

title = {(Sub)Symbolic × (a)symmetric × (non)combinatory: A map of AI approaches spanning symbolic/statistical to neural/ML},

author = {Paul S. Rosenbloom and Himanshu Joshi and Volkan Ustun},

url = {https://drive.google.com/file/d/1Ynp75A048Mfuh7e3kf_V7hs5kFD7uHsT/view},

year  = {2019},

date = {2019-12-01},

booktitle = {Proceedings of the 7th Annual Conference on Advances in Cognitive Systems},

pages = {113--131},

publisher = {Cognitive Systems Foundation},

address = {Cambridge, MA},

abstract = {The traditional symbolic versus subsymbolic dichotomy can be decomposed into three more basic dichotomies, to yield a 3D (2×2×2) space in which symbolic/statistical and neural/ML approaches to intelligence appear in opposite corners. Filling in all eight resulting cells then yields a map that spans a number of standard AI approaches plus a few that may be less familiar. Based on this map, four hypotheses are articulated, explored, and evaluated concerning its relevance to both a deeper understanding of the field of AI as a whole and the general capabilities required in complete AI/cognitive systems.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{hartholt_ubiquitous_2019,

title = {Ubiquitous Virtual Humans: A Multi-platform Framework for Embodied AI Agents in XR},

author = {Arno Hartholt and Ed Fast and Adam Reilly and Wendy Whitcup and Matt Liewer and Sharon Mozgai},

url = {https://ieeexplore.ieee.org/document/8942321/},

doi = {10.1109/AIVR46125.2019.00072},

isbn = {978-1-72815-604-0},

year  = {2019},

date = {2019-12-01},

booktitle = {Proceedings of the 2019 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)},

pages = {308--3084},

publisher = {IEEE},

address = {San Diego, CA, USA},

abstract = {We present an architecture and framework for the development of virtual humans for a range of computing platforms, including mobile, web, Virtual Reality (VR) and Augmented Reality (AR). The framework uses a mix of in-house and commodity technologies to support audio-visual sensing, speech recognition, natural language processing, nonverbal behavior generation and realization, text-to-speech generation, and rendering. This work builds on the Virtual Human Toolkit, which has been extended to support computing platforms beyond Windows. The resulting framework maintains the modularity of the underlying architecture, allows re-use of both logic and content through cloud services, and is extensible by porting lightweight clients. We present the current state of the framework, discuss how we model and animate our characters, and offer lessons learned through several use cases, including expressive character animation in seated VR, shared space and navigation in roomscale VR, autonomous AI in mobile AR, and real-time user performance feedback based on mobile sensors in headset AR.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{chawla_generating_2019,

title = {Generating Formality-Tuned Summaries Using Input-Dependent Rewards},

author = {Kushal Chawla and Balaji Vasan Srinivasan and Niyati Chhaya},

url = {https://www.aclweb.org/anthology/K19-1078},

doi = {10.18653/v1/K19-1078},

year  = {2019},

date = {2019-11-01},

booktitle = {Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)},

pages = {833--842},

publisher = {Association for Computational Linguistics},

address = {Hong Kong, China},

abstract = {Abstractive text summarization aims at generating human-like summaries by understanding and paraphrasing the given input content. Recent efforts based on sequence-to-sequence networks only allow the generation of a single summary. However, it is often desirable to accommodate the psycho-linguistic preferences of the intended audience while generating the summaries. In this work, we present a reinforcement learning based approach to generate formality-tailored summaries for an input article. Our novel input-dependent reward function aids in training the model with stylistic feedback on sampled and ground-truth summaries together. Once trained, the same model can generate formal and informal summary variants. Our automated and qualitative evaluations show the viability of the proposed framework.},

keywords = {ARO-Coop, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{soleymani_multimodal_2019,

title = {Multimodal Analysis and Estimation of Intimate Self-Disclosure},

author = {Mohammad Soleymani and Kalin Stefanov and Sin-Hwa Kang and Jan Ondras and Jonathan Gratch},

url = {http://dl.acm.org/citation.cfm?doid=3340555.3353737},

doi = {10.1145/3340555.3353737},

isbn = {978-1-4503-6860-5},

year  = {2019},

date = {2019-10-01},

booktitle = {Proceedings of the 2019 International Conference on Multimodal Interaction on - ICMI '19},

pages = {59--68},

publisher = {ACM Press},

address = {Suzhou, China},

abstract = {Self-disclosure to others has a proven benefit for one’s mental health. It is shown that disclosure to computers can be similarly beneficial for emotional and psychological well-being. In this paper, we analyzed verbal and nonverbal behavior associated with self-disclosure in two datasets containing structured human-human and human-agent interviews from more than 200 participants. Correlation analysis of verbal and nonverbal behavior revealed that linguistic features such as affective and cognitive content in verbal behavior, and nonverbal behavior such as head gestures are associated with intimate self-disclosure. A multimodal deep neural network was developed to automatically estimate the level of intimate self-disclosure from verbal and nonverbal behavior. Between modalities, verbal behavior was the best modality for estimating self-disclosure within-corpora achieving r = 0.66. However, the cross-corpus evaluation demonstrated that nonverbal behavior can outperform language modality in cross-corpus evaluation. Such automatic models can be deployed in interactive virtual agents or social robots to evaluate rapport and guide their conversational strategy.},

keywords = {MxR, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{haring_conflict_2019,

title = {Conflict Mediation in Human-Machine Teaming: Using a Virtual Agent to Support Mission Planning and Debriefing},

author = {Kerstin S. Haring and Jessica Tobias and Justin Waligora and Elizabeth Phillips and Nathan L Tenhundfeld and Gale LUCAS and Ewart J Visser and Jonathan GRATCH and Chad Tossell},

url = {https://ieeexplore.ieee.org/abstract/document/8956414},

doi = {10.1109/RO-MAN46459.2019.8956414},

year  = {2019},

date = {2019-10-01},

booktitle = {Proceedings of the 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)},

publisher = {IEEE},

address = {New Delhi, India},

abstract = {Socially intelligent artificial agents and robots are anticipated to become ubiquitous in home, work, and military environments. With the addition of such agents to human teams it is crucial to evaluate their role in the planning, decision making, and conflict mediation processes. We conducted a study to evaluate the utility of a virtual agent that provided mission planning support in a three-person human team during a military strategic mission planning scenario. The team consisted of a human team lead who made the final decisions and three supporting roles, two humans and the artificial agent. The mission outcome was experimentally designed to fail and introduced a conflict between the human team members and the leader. This conflict was mediated by the artificial agent during the debriefing process through discuss or debate and open communication strategies of conflict resolution [1]. Our results showed that our teams experienced conflict. The teams also responded socially to the virtual agent, although they did not find the agent beneficial to the mediation process. Finally, teams collaborated well together and perceived task proficiency increased for team leaders. Socially intelligent agents show potential for conflict mediation, but need careful design and implementation to improve team processes and collaboration.},

keywords = {Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{tavabi_multimodal_2019,

title = {Multimodal Learning for Identifying Opportunities for Empathetic Responses},

author = {Leili Tavabi and Kalin Stefanov and Setareh Nasihati Gilani and David Traum and Mohammad Soleymani},

url = {https://dl.acm.org/doi/10.1145/3340555.3353750},

doi = {10.1145/3340555.3353750},

isbn = {978-1-4503-6860-5},

year  = {2019},

date = {2019-10-01},

booktitle = {Proceedings of the 2019 International Conference on Multimodal Interaction},

pages = {95--104},

publisher = {ACM},

address = {Suzhou China},

abstract = {Embodied interactive agents possessing emotional intelligence and empathy can create natural and engaging social interactions. Providing appropriate responses by interactive virtual agents requires the ability to perceive users’ emotional states. In this paper, we study and analyze behavioral cues that indicate an opportunity to provide an empathetic response. Emotional tone in language in addition to facial expressions are strong indicators of dramatic sentiment in conversation that warrant an empathetic response. To automatically recognize such instances, we develop a multimodal deep neural network for identifying opportunities when the agent should express positive or negative empathetic responses. We train and evaluate our model using audio, video and language from human-agent interactions in a wizard-of-Oz setting, using the wizard’s empathetic responses and annotations collected on Amazon Mechanical Turk as ground-truth labels. Our model outperforms a textbased baseline achieving F1-score of 0.71 on a three-class classification. We further investigate the results and evaluate the capability of such a model to be deployed for real-world human-agent interactions.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ringeval_avec_2019,

title = {AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition},

author = {Fabien Ringeval and Eva-Maria Messner and Siyang Song and Shuo Liu and Ziping Zhao and Adria Mallol-Ragolta and Zhao Ren and Mohammad Soleymani and Maja Pantic and Björn Schuller and Michel Valstar and Nicholas Cummins and Roddy Cowie and Leili Tavabi and Maximilian Schmitt and Sina Alisamir and Shahin Amiriparian},

url = {http://dl.acm.org/citation.cfm?doid=3347320.3357688},

doi = {10.1145/3347320.3357688},

isbn = {978-1-4503-6913-8},

year  = {2019},

date = {2019-10-01},

booktitle = {Proceedings of the 9th International on Audio/Visual Emotion Challenge and Workshop - AVEC '19},

pages = {3--12},

publisher = {ACM Press},

address = {Nice, France},

abstract = {The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) 'State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition' is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions. The goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the health and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of various approaches to health and emotion recognition from real-life data. This paper presents the major novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline systems on the three proposed tasks: state-of-mind recognition, depression assessment with AI, and cross-cultural affect sensing, respectively.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{hartholt_virtual_2019-1,

title = {Virtual Humans in Augmented Reality: A First Step towards Real-World Embedded Virtual Roleplayers},

author = {Arno Hartholt and Sharon Mozgai and Ed Fast and Matt Liewer and Adam Reilly and Wendy Whitcup and Albert "Skip" Rizzo},

url = {http://dl.acm.org/citation.cfm?doid=3349537.3352766},

doi = {10.1145/3349537.3352766},

isbn = {978-1-4503-6922-0},

year  = {2019},

date = {2019-10-01},

booktitle = {Proceedings of the 7th International Conference on Human-Agent Interaction - HAI '19},

pages = {205--207},

publisher = {ACM Press},

address = {Kyoto, Japan},

abstract = {We present one of the first applications of virtual humans in Augmented Reality (AR), which allows young adults with Autism Spectrum Disorder (ASD) the opportunity to practice job interviews. It uses the Magic Leap’s AR hardware sensors to provide users with immediate feedback on six different metrics, including eye gaze, blink rate and head orientation. The system provides two characters, with three conversational modes each. Ported from an existing desktop application, the main development lessons learned were: 1) provide users with navigation instructions in the user interface, 2) avoid dark colors as they are rendered transparently, 3) use dynamic gaze so characters maintain eye contact with the user, 4) use hardware sensors like eye gaze to provide user feedback, and 5) use surface detection to place characters dynamically in the world.},

keywords = {MedVR, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{gordon_wizard_2019,

title = {A Wizard of Oz Data Collection Framework for Internet of Things Dialogues},

author = {Carla Gordon and Volodymyr Yanov and David Traum and Kallirroi Georgila},

url = {http://semdial.org/anthology/papers/Z/Z19/Z19-4024/},

year  = {2019},

date = {2019-09-01},

booktitle = {Proceedings of the 23rd Workshop on the Semantics and Pragmatics of Dialogue - Poster Abstracts},

pages = {3},

publisher = {SEMDIAL},

address = {London, UK},

abstract = {We describe a novel Wizard of Oz dialogue data collection framework in the Internet of Things domain. Our tool is designed for collecting dialogues between a human user, and 8 different system profiles, each with a different communication strategy. We then describe the data collection conducted with this tool, as well as the dialogue corpus that was generated.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{mell_likeability-success_2019,

title = {The Likeability-Success Tradeoff: Results of the 2nd Annual Human-Agent Automated Negotiating Agents Competition},

author = {Johnathan Mell and Jonathan Gratch and Reyhan Aydogan and Tim Baarslag and Catholijn M. Jonker},

url = {https://ieeexplore.ieee.org/xpl/conhome/8911251/proceeding},

year  = {2019},

date = {2019-09-01},

booktitle = {Proceedings of the 8th International Conference on Affective Computing and Intelligent Interaction (ACII)},

publisher = {IEEE},

address = {Cambridge, UK},

abstract = {We present the results of the 2nd Annual Human-Agent League of the Automated Negotiating Agent Competition. Building on the success of the previous year’s results, a new challenge was issued that focused exploring the likeability-success tradeoff in negotiations. By examining a series of repeated negotiations, actions may affect the relationship between automated negotiating agents and their human competitors over time. The results presented herein support a more complex view of human-agent negotiation and capture of integrative potential (win-win solutions). We show that, although likeability is generally seen as a tradeoff to winning, agents are able to remain well-liked while winning if integrative potential is not discovered in a given negotiation. The results indicate that the top-performing agent in this competition took advantage of this loophole by engaging in favor exchange across negotiations (cross-game logrolling). These exploratory results provide information about the effects of different submitted “black-box” agents in humanagent negotiation and provide a state-of-the-art benchmark for human-agent design.},

keywords = {Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{hoegen_signals_2019,

title = {Signals of Emotion Regulation in a Social Dilemma: Detection from Face and Context},

author = {Rens Hoegen and Jonathan Gratch and Brian Parkinson and Danielle Shore},

url = {https://ieeexplore.ieee.org/xpl/conhome/8911251/proceeding},

year  = {2019},

date = {2019-09-01},

booktitle = {Proceedings of the 8th International Conference on Affective Computing and Intelligent Interaction (ACII) roceedings of the},

pages = {7},

publisher = {IEEE},

address = {Cambridge, UK},

abstract = {In social decision-making tasks, facial expressions are informative signals that indicate motives and intentions. As people are aware that their expressions influence partner behavior, expressions may be strategically regulated in competitive environments to influence a social partner’s decisionmaking. In this work, we examine facial expressions and their strategic regulation within the context of an iterated prisoner’s dilemma. Utilizing video-cued rating procedures, we examine several key questions about the functionality of facial expressions in social decision-making. First, we assess the extent to which emotion and expression regulation are accurately detected from dynamic facial expressions in interpersonal interactions. Second, we explore which facial cues are utilized to evaluate emotion and regulation information. Finally, we investigate the role of context in participants’ emotion and regulation judgments. Results show that participants accurately perceive facial emotion and expression regulation, although they are better at recognizing emotions than regulation. Using automated expression analysis and stepwise regression, we constructed models that use action units from participant videos to predict their video-cued emotion and regulation ratings. We show that these models perform similarly and, in some cases, better than participants do. Moreover, these models demonstrate that game state information improves predictive accuracy, thus implying that context information is important in the evaluation of facial expressions.},

keywords = {Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lei_smiles_2019,

title = {Smiles Signal Surprise in a Social Dilemma},

author = {Su Lei and Jonathan Gratch},

url = {https://ieeexplore.ieee.org/xpl/conhome/8911251/proceeding},

year  = {2019},

date = {2019-09-01},

booktitle = {Proceedings of the 8th International Conference on Affective Computing and Intelligent Interaction (ACII)},

publisher = {IEEE},

address = {Cambridge, UK},

abstract = {This study examines spontaneous facial expressions in an iterated prisoner’s dilemma with financial stakes. Our goal was to identify typical facial expressions associated with key events during the interaction (e.g., cooperation or exploitation) and contrast these reactions with alternative theories of the meaning of facial expressions. Specifically, we examined if expressions reflect individual self-interest (e.g., winning) or social motives (e.g., promoting fairness) and the extent to which surprise might moderate the intensity of facial displays. In contrast to predictions of scientific and folk theories of expression, smiles were the only expressions consistently elicited, regardless of the reward or fairness of outcomes. Further, these smiles serve as a reliable indicator of the surprisingness of the event, but not its pleasure (contradicting research on both the meaning of smiles and indicators of surprise). To our knowledge, this is the first study to indicate that smiles signal surprise.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lycan_direct_2019,

title = {Direct and Mediated Interaction with a Holocaust Survivor},

author = {Bethany Lycan and Ron Artstein},

url = {https://doi.org/10.1007/978-3-319-92108-2_17},

doi = {10.1007/978-3-319-92108-2_17},

year  = {2019},

date = {2019-08-01},

booktitle = {Proceedings of the Advanced Social Interaction with Agents: 8th International Workshop on Spoken Dialog Systems},

volume = {510},

pages = {161--167},

publisher = {Springer},

address = {Cham, Switzerland},

series = {Lecture Notes in Electrical Engineering},

abstract = {The New Dimensions in Testimony dialogue system was placed in two museums under two distinct conditions: docent-led group interaction, and free interaction with visitors. Analysis of the resulting conversations shows that docent-led interactions have a lower vocabulary and a higher proportion of user utterances that directly relate to the system’s subject matter, while free interaction is more personal in nature. Under docent-led interaction the system gives a higher proportion of direct appropriate responses, but overall correct system behavior is about the same in both conditions because the free interaction condition has more instances where the correct system behavior is to avoid a direct response.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bonial_augmenting_2019,

title = {Augmenting Abstract Meaning Representation for Human-Robot Dialogue},

author = {Claire Bonial and Lucia Donatelli and Stephanie M. Lukin and Stephen Tratz and Ron Artstein and David Traum and Clare R. Voss},

url = {https://www.aclweb.org/anthology/W19-3322},

year  = {2019},

date = {2019-08-01},

booktitle = {Proceedings of the First International Workshop on Designing Meaning Representations (DMR)},

pages = {199--210},

publisher = {Association of Computational Linguistics},

address = {Florence, Italy},

abstract = {We detail refinements made to Abstract Meaning Representation (AMR) that make the representation more suitable for supporting a situated dialogue system, where a human remotely controls a robot for purposes of search and rescue and reconnaissance. We propose 36 augmented AMRs that capture speech acts, tense and aspect, and spatial information. This linguistic information is vital for representing important distinctions, for example whether the robot has moved, is moving, or will move. We evaluate two existing AMR parsers for their performance on dialogue data. We also outline a model for graph-to-graph conversion, in which output from AMR parsers is converted into our refined AMRs. The design scheme presentedhere,thoughtask-specific,isextendable for broad coverage of speech acts using AMR in future task-independent work.},

keywords = {ARL, DoD, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nasihati_gilani_can_2019,

title = {Can a Signing Virtual Human Engage a Baby's Attention?},

author = {Setareh Nasihati Gilani and David Traum and Rachel Sortino and Grady Gallagher and Kailyn Aaron-Lozano and Cryss Padilla and Ari Shapiro and Jason Lamberton and Laura-Ann Petitto},

url = {http://dl.acm.org/citation.cfm?doid=3308532.3329463},

doi = {10.1145/3308532.3329463},

isbn = {978-1-4503-6672-4},

year  = {2019},

date = {2019-07-01},

booktitle = {Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents - IVA '19},

pages = {162--169},

publisher = {ACM Press},

address = {Paris, France},

abstract = {The child developmental period of ages 6-12 months marks a widely understood “critical period” for healthy language learning, during which, failure to receive exposure to language can place babies at risk for language and reading problems spanning life. Deaf babies constitute one vulnerable population as they can experience dramatically reduced or no access to usable linguistic input during this period. Technology has been used to augment linguistic input (e.g., auditory devices; language videotapes) but research finds limitations in learning. We evaluated an AI system that uses an Avatar (provides language and socially contingent interactions) and a robot (aids attention to the Avatar) to facilitate infants’ ability to learn aspects of American Sign Language (ASL), and asked three questions: (1) Can babies with little/no exposure to ASL distinguish among the Avatar’s different conversational modes (Linguistic Nursery Rhymes; Social Gestures; Idle/nonlinguistic postures; 3rd person observer)? (2) Can an Avatar stimulate babies’ production of socially contingent responses, and crucially, nascent language responses? (3) What is the impact of parents’ presence/absence of conversational participation? Surprisingly, babies (i) spontaneously distinguished among Avatar conversational modes, (ii) produced varied socially contingent responses to Avatar’s modes, and (iii) parents influenced an increase in babies’ response tokens to some Avatar modes, but the overall categories and pattern of babies’ behavioral responses remained proportionately similar irrespective of parental participation. Of note, babies produced the greatest percentage of linguistic responses to the Avatar’s Linguistic Nursery Rhymes versus other Avatar conversational modes. This work demonstrates the potential for Avatars to facilitate language learning in young babies.},

keywords = {Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{hartholt_virtual_2019,

title = {Virtual Job Interviewing Practice for High-Anxiety Populations},

author = {Arno Hartholt and Sharon Mozgai and Albert "Skip" Rizzo},

url = {http://dl.acm.org/citation.cfm?doid=3308532.3329417},

doi = {10.1145/3308532.3329417},

isbn = {978-1-4503-6672-4},

year  = {2019},

date = {2019-07-01},

booktitle = {Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents - IVA '19},

pages = {238--240},

publisher = {ACM Press},

address = {Paris, France},

abstract = {We present a versatile system for training job interviewing skills that focuses specifically on segments of the population facing increased challenges during the job application process. In particular, we target those with Autism Spectrum Disorder (ADS), veterans transitioning to civilian life, and former convicts integrating back into society. The system itself follows the SAIBA framework and contains several interviewer characters, who each represent a different type of vocational field, (e.g. service industry, retail, office, etc.) Each interviewer can be set to one of three conversational modes, which not only affects what they say and how they say it, but also their supporting body language. This approach offers varying difficulties, allowing users to start practicing with interviewers who are more encouraging and accommodating before moving on to personalities that are more direct and indifferent. Finally, the user can place the interviewers in different environmental settings (e.g. conference room, restaurant, executive office, etc.), allowing for many different combinations in which to practice.},

keywords = {MedVR, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{mell_expert-model_2019,

title = {An Expert-Model & Machine Learning Hybrid Approach to Predicting Human-Agent Negotiation Outcomes},

author = {Johnathan Mell and Markus Beissinger and Jonathan Gratch},

url = {http://dl.acm.org/citation.cfm?doid=3308532.3329433},

doi = {10.1145/3308532.3329433},

isbn = {978-1-4503-6672-4},

year  = {2019},

date = {2019-07-01},

booktitle = {Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents - IVA '19},

pages = {212--214},

publisher = {ACM Press},

address = {Paris, France},

abstract = {We present the results of a machine-learning approach to the analysis of several human-agent negotiation studies. By combining expert knowledge of negotiating behavior compiled over a series of empirical studies with neural networks, we show that a hybrid approach to parameter selection yields promise for designing -more effective and socially intelligent agents. Specifically, we show that a deep feedforward neural network using a theory-driven three-parameter model can be effective in predicting negotiation outcomes. Furthermore, it outperforms other expert-designed models that use more parameters, as well as those using other, more limited techniques (such as linear regression models or boosted decision trees). We anticipate these results will have impact for those seeking to combine extensive domain knowledge with more automated approaches in human-computer negotiation.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lee_whats_2019,

title = {What's on Your Virtual Mind?: Mind Perception in Human-Agent Negotiations},

author = {Minha Lee and Gale Lucas and Johnathan Mell and Emmanuel Johnson and Jonathan Gratch},

url = {http://dl.acm.org/citation.cfm?doid=3308532.3329465},

doi = {10.1145/3308532.3329465},

isbn = {978-1-4503-6672-4},

year  = {2019},

date = {2019-07-01},

booktitle = {Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents - IVA '19},

pages = {38--45},

publisher = {ACM Press},

address = {Paris, France},

abstract = {Recent research shows that how we respond to other social actors depends on what sort of mind we ascribe to them. In this article we examine how perceptions of a virtual agent’s mind shape behavior in human-agent negotiations. We varied descriptions and communicative behavior of virtual agents on two dimensions according to the mind perception theory: agency (cognitive aptitude) and patiency (affective aptitude). Participants then engaged in negotiations with the different agents. People scored more points and engaged in shorter negotiations with agents described to be cognitively intelligent, and got lower points and had longer negotiations with agents that were described to be cognitively unintelligent. Accordingly, agents described as having low-agency ended up earning more points than those with high-agency. Within the negotiations themselves, participants sent more happy and surprise emojis and emotionally valenced messages to agents described to be emotional. This high degree of described patiency also affected perceptions of the agent’s moral standing and relatability. In short, manipulating the perceived mind of agents affects how people negotiate with them. We discuss these results, which show that agents are perceived not only as social actors, but as intentional actors through negotiations.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rosenbloom_architectural_2019,

title = {An Architectural Integration of Temporal Motivation Theory for Decision Making},

author = {Paul S Rosenbloom and Volkan Ustun},

url = {https://iccm-conference.neocities.org/2019/proceedings/papers/ICCM2019_paper_7.pdf},

year  = {2019},

date = {2019-07-01},

booktitle = {In Proceedings of the 17thAnnual Meeting of the International Conference on Cognitive Modeling},

pages = {6},

address = {Montreal, Canada},

abstract = {Temporal Motivation Theory (TMT) is incorporated into the Sigma cognitive architecture to explore the ability of this combination to yield human-like decision making. In conjunction with Lazy Reinforcement Learning (LRL), which provides the inputs required for this form of decision making, experiments are run on a simple reinforcement learning task, a preference reversal task, and an uncertain two-choice task.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rosenbloom_symmetry_2019,

title = {(A)symmetry × (Non)monotonicity: Towards a Deeper Understanding of Key Cognitive Di/Trichotomies and the Common Model of Cognition},

author = {Paul S Rosenbloom},

url = {https://iccm-conference.neocities.org/2019/proceedings/papers/ICCM2019_paper_6.pdf},

year  = {2019},

date = {2019-07-01},

booktitle = {In Proceedings of the 17th Annual Meeting of the International Conference on Cognitive Modeling},

pages = {6},

address = {Montreal, Canada},

abstract = {A range of dichotomies from across the cognitive sciences are reduced to either (a)symmetry or (non)monotonicity. Taking the cross-product of these two elemental dichotomies then yields a deeper understanding of both two key trichotomies –based on control and content hierarchies – and the Common Model of Cognition, with results that bear on the structure of integrative cognitive architectures, models and systems, and on their commonalities, differences and gaps.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{johnson_assessing_2019,

title = {Assessing Common Errors Students Make When Negotiating},

author = {Emmanuel Johnson and Sarah Roediger and Gale Lucas and Jonathan Gratch},

url = {http://dl.acm.org/citation.cfm?doid=3308532.3329470},

doi = {10.1145/3308532.3329470},

isbn = {978-1-4503-6672-4},

year  = {2019},

date = {2019-07-01},

booktitle = {Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents - IVA '19},

pages = {30--37},

publisher = {ACM Press},

address = {Paris, France},

abstract = {Research has shown that virtual agents can be effective tools for teaching negotiation. Virtual agents provide an opportunity for students to practice their negotiation skills which leads to better outcomes. However, these negotiation training agents often lack the ability to understand the errors students make when negotiating, thus limiting their effectiveness as training tools. In this article, we argue that automated opponent-modeling techniques serve as effective methods for diagnosing important negotiation mistakes. To demonstrate this, we analyze a large number of participant traces generated while negotiating with a set of automated opponents. We show that negotiators’ performance is closely tied to their understanding of an opponent’s preferences. We further show that opponent modeling techniques can diagnose specific errors including: failure to elicit diagnostic information from an opponent, failure to utilize the information that was elicited, and failure to understand the transparency of an opponent. These results show that opponent modeling techniques can be effective methods for diagnosing and potentially correcting crucial negotiation errors.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lucas_effectiveness_2019,

title = {The Effectiveness of Social Influence Tactics when Used by a Virtual Agent},

author = {Gale M. Lucas and Janina Lehr and Nicole Krämer and Jonathan Gratch},

url = {http://dl.acm.org/citation.cfm?doid=3308532.3329464},

doi = {10.1145/3308532.3329464},

isbn = {978-1-4503-6672-4},

year  = {2019},

date = {2019-07-01},

booktitle = {Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents - IVA '19},

pages = {22--29},

publisher = {ACM Press},

address = {Paris, France},

abstract = {Research in social science distinguishes between two types of social influence: informational and normative. Informational social influence is driven by the desire to evaluate ambiguous situations correctly, whereas normative social influence is driven by the desire to be liked and gain social acceptance from another person. Although we know from research that humans can effectively use either of these techniques to persuade other humans, scholars have yet to examine the relative effectiveness of informational versus normative social influence when used by virtual agents. We report a study in which users interact with a system that persuades them either using informational or normative social influence. Furthermore, to compare agents to human interlocutors, users are told that the system is either teleoperated by a human (avatar) or fully-automated (agent). Using this design, we are able to compare the effectiveness of virtual agents (vs humans) in employing informational versus normative social influence. Participants interacted with the system, which employed a Wizard-of-Oz operated virtual agent that tried to persuade the user to agree with its rankings on a “survival task.” Controlling for initial divergence in rankings between user and the agent, there was a significant main effect such that informational social influence resulted in greater influence than normative influence. However, this was qualified by an interaction that approached significance; users were, if anything, more persuaded by informational influence when they believe the agent was AI (compared to a human), whereas there was no difference between the agent and avatar in the normative influence condition.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{sohail_blissymbolics_2019,

title = {A Blissymbolics Translation System},

author = {Usman Sohail and David Traum},

url = {http://aclweb.org/anthology/W19-1705},

doi = {10.18653/v1/W19-1705},

year  = {2019},

date = {2019-06-01},

booktitle = {Proceedings of the Eighth Workshop on Speech and Language Processing for Assistive Technologies},

pages = {32--36},

publisher = {Association for Computational Linguistics},

address = {Minneapolis, Minnesota},

abstract = {Blissymbolics (Bliss) is a pictographic writing system that is used by people with communication disorders. Bliss attempts to create a writing system that makes words easier to distinguish by using pictographic symbols that encapsulate meaning rather than sound, as the English alphabet does for example. Users of Bliss rely on human interpreters to use Bliss. We created a translation system from Bliss to natural English with the hopes of decreasing the reliance on human interpreters by the Bliss community. We first discuss the basic rules of Blissymbolics. Then we point out some of the challenges associated with developing computer assisted tools for Blissymbolics. Next we talk about our ongoing work in developing a translation system, including current limitations, and future work. We conclude with a set of examples showing the current capabilities of our translation system.},

keywords = {Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{song_polysemous_2019,

title = {Polysemous Visual-Semantic Embedding for Cross-Modal Retrieval},

author = {Yale Song and Mohammad Soleymani},

url = {https://arxiv.org/abs/1906.04402},

year  = {2019},

date = {2019-06-01},

booktitle = {Proceedings of the 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

pages = {10},

publisher = {IEEE},

address = {Long Beach, CA},

abstract = {Visual-semantic embedding aims to find a shared latent space where related visual and textual instances are close to each other. Most current methods learn injective embedding functions that map an instance to a single point in the shared space. Unfortunately, injective embedding cannot effectively handle polysemous instances with multiple possible meanings; at best, it would find an average representation of different meanings. This hinders its use in real-world scenarios where individual instances and their cross-modal associations are often ambiguous. In this work, we introduce Polysemous Instance Embedding Networks (PIE-Nets) that compute multiple and diverse representations of an instance by combining global context with locally-guided features via multi-head self-attention and residual learning. To learn visual-semantic embedding, we tie-up two PIE-Nets and optimize them jointly in the multiple instance learning framework. Most existing work on cross-modal retrieval focus on image-text pairs of data. Here, we also tackle a more challenging case of video-text retrieval. To facilitate further research in video-text retrieval, we release a new dataset of 50K video-sentence pairs collected from social media, dubbed MRW (my reaction when). We demonstrate our approach on both image-text and video-text retrieval scenarios using MS-COCO, TGIF, and our new MRW dataset.},

keywords = {UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{gilani_can_2019,

title = {Can a Virtual Human Facilitate Language Learning in a Young Baby?},

author = {Setareh Nasihati Gilani and David Traum and Rachel Sortino and Grady Gallagher and Kailyn Aaron-lozano and Cryss Padilla and Ari Shapiro and Jason Lamberton and Laura-ann Petitto},

url = {https://dl.acm.org/citation.cfm?id=3332035},

isbn = {978-1-4503-6309-9},

year  = {2019},

date = {2019-05-01},

booktitle = {Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems},

publisher = {ACM},

address = {Montreal, Canada},

abstract = {There is a significant paucity of work on language learning systems for young infants [2, 5, 19] despite the widely understood critical importance that this developmental period has for healthy language and cognitive growth, and related reading and academic success [6, 14]. Deaf babies constitute one vulnerable population as they can experience dramatically reduced or no access to usable linguistic input during this period [18]. This causes potentially devastating impact on children's linguistic, cognitive, and social skills [9, 10, 15, 16, 20]. We introduced an AI system, called RAVE (Robot, AVatar, thermal Enhanced language learning tool), designed specifically for babies within the age range of 6-12 months [8, 17]. RAVE consists of two agents: a virtual human (provides language and socially contingent interactions) and an embodied robot (provides socially engaging physical cues to babies and directs babies' attention to the virtual human). Detailed description of the system's constituent components and dialogue algorithms are presented in [17] and [8].},

keywords = {Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{koeman_recognising_2019,

title = {Recognising and Explaining Bidding Strategies in Negotiation Support Systems},

author = {Vincent J Koeman and Koen V Hindriks and Jonathan Gratch and Catholijn M Jonker},

url = {https://dl.acm.org/citation.cfm?id=3332011},

isbn = {978-1-4503-6309-9},

year  = {2019},

date = {2019-05-01},

booktitle = {Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems},

pages = {3},

publisher = {ACM},

address = {Montreal, Canada},

abstract = {To improve a negotiator's ability to recognise bidding strategies, we pro-actively provide explanations that are based on the opponent's bids and the negotiator's guesses about the opponent's strategy. We introduce an aberration detection mechanism for recognising strategies and the notion of an explanation matrix. The aberration detection mechanism identifies when a bid falls outside the range of expected behaviour for a specific strategy. The explanation matrix is used to decide when to provide what explanations. We evaluated our work experimentally in a task in which participants are asked to identify their opponent's strategy in the environment of a negotiation support system, namely the Pocket Negotiator (PN). We implemented our explanation mechanism in the PN and experimented with different explanation matrices. As the number of correct guesses increases with explanations, indirectly, these experiments show the effectiveness of our aberration detection mechanism. Our experiments with over 100 participants show that suggesting consistent strategies is more effective than explaining why observed behaviour is inconsistent.},

keywords = {Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}