@inproceedings{bonial_dialogue-amr_2020,

title = {Dialogue-AMR: Abstract Meaning Representation for Dialogue},

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

url = {https://www.aclweb.org/anthology/2020.lrec-1.86/},

year  = {2020},

date = {2020-05-01},

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

pages = {12},

publisher = {European Language Resources Association},

address = {Marseille, France},

abstract = {This paper describes a schema that enriches Abstract Meaning Representation (AMR) in order to provide a semantic representation for facilitating Natural Language Understanding (NLU) in dialogue systems. AMR offers a valuable level of abstraction of the propositional content of an utterance; however, it does not capture the illocutionary force or speaker’s intended contribution in the broader dialogue context (e.g., make a request or ask a question), nor does it capture tense or aspect. We explore dialogue in the domain of human-robot interaction, where a conversational robot is engaged in search and navigation tasks with a human partner. To address the limitations of standard AMR, we develop an inventory of speech acts suitable for our domain, and present “Dialogue-AMR”, an enhanced AMR that represents not only the content of an utterance, but the illocutionary force behind it, as well as tense and aspect. To showcase the coverage of the schema, we use both manual and automatic methods to construct the “DialAMR” corpus—a corpus of human-robot dialogue annotated with standard AMR and our enriched Dialogue-AMR schema. Our automated methods can be used to incorporate AMR into a larger NLU pipeline supporting human-robot dialogue.},

keywords = {ARL, ARO-Coop, DoD, UARC, 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{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{chaffey_developing_2019,

title = {Developing a Virtual Reality Wildfire Simulation to Analyze Human Communication and Interaction with a Robotic Swarm During Emergencies},

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 David Traum},

url = {http://www-scf.usc.edu/ nasihati/publications/HLTCEM_2019.pdf},

year  = {2019},

date = {2019-05-01},

booktitle = {Proceedings of the 9th Language and Technology Conference},

publisher = {LTC},

address = {Poznań, Poland},

abstract = {Search and rescue missions involving robots face multiple challenges. The ratio of operators to robots is frequently one to one or higher, operators tasked with robots must contend with cognitive overload for long periods, and the robots themselves may be discomfiting to located survivors. To improve on the current state, we propose a swarm of robots equipped with natural language abilities and guided by a central virtual “spokesperson” able to access “plays”. The spokesperson may assist the operator with tasking the robots in their exploration of a zone, which allows the operator to maintain a safe distance. The use of multiple robots enables rescue personnel to cover a larger swath of ground, and the natural language component allows the robots to communicate with survivors located on site. This capability frees the operator to handle situations requiring personal attention, and overall can accelerate the location and assistance of survivors. In order to develop this system, we are creating a virtual reality simulation, in order to conduct a study and analysis of how humans communicate with these swarms of robots. The data collected from this experiment will inform how to best design emergency response swarm robots that are effectively able to communicate with the humans around them.},

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

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{artstein_digital_2019,

title = {Digital Survivor of Sexual Assault},

author = {Ron Artstein and Carla Gordon and Usman Sohail and Chirag Merchant and Andrew Jones and Julia Campbell and Matthew Trimmer and Jeffrey Bevington and COL Christopher Engen and David Traum},

url = {https://doi.org/10.1145/3301275.3302303},

doi = {10.1145/3301275.3302303},

isbn = {978-1-4503-6272-6},

year  = {2019},

date = {2019-03-01},

booktitle = {Proceedings of the 24th International Conference on Intelligent User Interfaces},

pages = {417–425},

publisher = {ACM},

address = {Marina del Rey, California},

abstract = {The Digital Survivor of Sexual Assault (DS2A) is an interface that allows a user to have a conversational experience with a survivor of sexual assault, using Artificial Intelligence technology and recorded videos. The application uses a statistical classifier to retrieve contextually appropriate pre-recorded video utterances by the survivor, together with dialogue management policies which enable users to conduct simulated conversations with the survivor about the sexual assault, its aftermath, and other pertinent topics. The content in the application has been specifically elicited to support the needs for the training of U.S. Army professionals in the Sexual Harassment/Assault Response and Prevention (SHARP) Program, and the application comes with an instructional support package. The system has been tested with approximately 200 users, and is presently being used in the SHARP Academy's capstone course.},

keywords = {DoD, Graphics, MedVR, UARC, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{marge_balancing_2018,

title = {Balancing Efficiency and Coverage in Human-Robot Dialogue Collection},

author = {Matthew Marge and Claire Bonial and Stephanie M. Lukin and Cory J. Hayes and Ashley Foots and Ron Artstein and Cassidy Henry and Kimberly A. Pollard and Carla Gordon and Felix Gervits and Anton Leuski and Susan G. Hill and Clare R. Voss and David Traum},

url = {https://arxiv.org/abs/1810.02017},

year  = {2018},

date = {2018-10-01},

booktitle = {Proceedings of the AAAI Fall Symposium on Interactive Learning in Artificial Intelligence for Human-Robot Interaction},

publisher = {arXiv},

address = {Arlington, Virginia},

abstract = {We describe a multi-phased Wizard-of-Oz approach to collecting human-robot dialogue in a collaborative search and navigation task. The data is being used to train an initial automated robot dialogue system to support collaborative exploration tasks. In the first phase, a wizard freely typed robot utterances to human participants. For the second phase, this data was used to design a GUI that includes buttons for the most common communications, and templates for communications with varying parameters. Comparison of the data gathered in these phases show that the GUI enabled a faster pace of dialogue while still maintaining high coverage of suitable responses, enabling more efficient targeted data collection, and improvements in natural language understanding using GUI-collected data. As a promising first step towardsinteractivelearning,thisworkshowsthatourapproach enables the collection of useful training data for navigationbased HRI tasks.},

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

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lukin_consequences_2018,

title = {Consequences and Factors of Stylistic Differences in Human-Robot Dialogue},

author = {Stephanie M. Lukin and Kimberly A. Pollard and Claire Bonial and Matthew Marge and Cassidy Henry and Ron Artstein and David Traum and Clare R. Voss},

url = {https://www.aclweb.org/anthology/papers/W/W18/W18-5012/},

doi = {10.18653/v1/W18-5012},

year  = {2018},

date = {2018-07-01},

booktitle = {Proceedings of the SIGDIAL 2018 Conference},

pages = {110–118},

publisher = {Association for Computational Linguistics},

address = {Melbourne, Australia},

abstract = {This paper identifies stylistic differences in instruction-giving observed in a corpus of human-robot dialogue. Differences in verbosity and structure (i.e., single-intent vs. multi-intent instructions) arose naturally without restrictions or prior guidance on how users should speak with the robot. Different styles were found to produce different rates of miscommunication, and correlations were found between style differences and individual user variation, trust, and interaction experience with the robot. Understanding potential consequences and factors that influence style can inform design of dialogue systems that are robust to natural variation from human users.},

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

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{traum_dialogue_2018,

title = {Dialogue Structure Annotation for Multi-Floor Interaction},

author = {David Traum and Cassidy Henry and Stephanie Lukin and Ron Artstein and Felix Gervitz and Kim Pollard and Claire Bonial and Su Lei and Clare R. Voss and Matthew Marge and Cory J. Hayes and Susan G. Hill},

url = {http://www.lrec-conf.org/proceedings/lrec2018/summaries/672.html},

isbn = {979-10-95546-00-9},

year  = {2018},

date = {2018-05-01},

booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},

pages = {104–111},

publisher = {ELRA},

address = {Miyazaki, Japan},

abstract = {We present an annotation scheme for meso-level dialogue structure, specifically designed for multi-floor dialogue. The scheme includes a transaction unit that clusters utterances from multiple participants and floors into units according to realization of an initiator’s intent, and relations between individual utterances within the unit. We apply this scheme to annotate a corpus of multi-floor human-robot interaction dialogues. We examine the patterns of structure observed in these dialogues and present inter-annotator statistics and relative frequencies of types of relations and transaction units. Finally, some example applications of these annotations are introduced.},

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

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{bonial_human-robot_2018,

title = {Human-Robot Dialogue and Collaboration in Search and Navigation},

author = {Claire Bonial and Stephanie M. Lukin and Ashley Foots and Cassidy Henry and Matthew Marge and Kimberly A. Pollard and Ron Artstein and David Traum and Clare R. Voss},

url = {http://www.areaworkshop.org/wp-content/uploads/2018/05/4.pdf},

year  = {2018},

date = {2018-05-01},

booktitle = {Proceedings of the AREA Workshop: Annotation, Recognition, and Evaluation of Actions},

publisher = {AREA 2018},

address = {Miyazaki, Japan},

abstract = {Collaboration with a remotely located robot in tasks such as disaster relief and search and rescue can be facilitated by grounding natural language task instructions into actions executable by the robot in its current physical context. The corpus we describe here provides insight into the translation and interpretation a natural language instruction undergoes starting from verbal human intent, to understanding and processing, and ultimately, to robot execution. We use a ‘Wizard-of-Oz’ methodology to elicit the corpus data in which a participant speaks freely to instruct a robot on what to do and where to move through a remote environment to accomplish collaborativesearchandnavigationtasks. Thisdataoffersthepotentialforexploringandevaluatingactionmodelsbyconnectingnatural language instructions to execution by a physical robot (controlled by a human ‘wizard’). In this paper, a description of the corpus (soon to be openly available) and examples of actions in the dialogue are provided.},

keywords = {ARL, DoD, Virtual Humans},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{marge_exploring_2017,

title = {Exploring Variation of Natural Human Commands to a Robot in a Collaborative Navigation Task},

author = {Matthew Marge and Claire Bonial and Ashley Foots and Cory Hayes and Cassidy Henry and Kimberly Pollard and Ron Artstein and Clare Voss and David Traum},

url = {http://www.aclweb.org/anthology/W17-2808},

year  = {2017},

date = {2017-08-01},

booktitle = {Proceedings of the First Workshop on Language Grounding for Robotics},

pages = {58–66},

publisher = {Association for Computational Linguistics},

address = {Vancouver, Canada},

abstract = {Robot-directed communication is variable, and may change based on human perception of robot capabilities. To collect training data for a dialogue system and to investigate possible communication changes over time, we developed a Wizard-of-Oz study that (a) simulates a robot’s limited understanding, and (b) collects dialogues where human participants build a progressively better mental model of the robot’s understanding. With ten participants, we collected ten hours of human-robot dialogue. We analyzed the structure of instructions that participants gave to a remote robot before it responded. Our findings show a general initial preference for including metric information (e.g., move forward 3 feet) over landmarks (e.g., move to the desk) in motion commands, but this decreased over time, suggesting changes in perception.},

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

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{cassidy_towards_2017,

title = {Towards Efficient Human-Robot Dialogue Collection: Moving Fido into the VirtualWorld},

author = {Henry Cassidy and Pooja Moolchandani and Kimberly A. Pollard and Claire Bonial and Ashley Foots and Ron Artstein and Cory Hayes and Claire R. Voss and David Traum and Matthew Marge},

url = {http://www.winlp.org/wp-content/uploads/2017/final_papers_2017/52_Paper.pdf},

year  = {2017},

date = {2017-07-01},

booktitle = {Proceedings of the WiNLP workshop},

address = {Vancouver, Canada},

abstract = {Our research aims to develop a natural dialogue interface between robots and humans. We describe two focused efforts to increase data collection efficiency towards this end: creation of an annotated corpus of interaction data, and a robot simulation, allowing greater flexibility in when and where we can run experiments.},

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

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{marge_assessing_2016,

title = {Assessing Agreement in Human-Robot Dialogue Strategies: A Tale of TwoWizards},

author = {Matthew Marge and Claire Bonial and Kimberly A. Pollard and Ron Artstein and Brendan Byrne and Susan G. Hill and Clare Voss and David Traum},

url = {http://iva2016.ict.usc.edu/wp-content/uploads/Papers/100110460.pdf},

year  = {2016},

date = {2016-09-01},

booktitle = {Proceedings of The Sixteenth International Conference on Intelligent Virtual Agents (IVA 2016),},

publisher = {Springer},

address = {Los Angeles, CA},

abstract = {The Wizard-of-Oz (WOz) method is a common experimental technique in virtual agent and human-robot dialogue research for eliciting natural communicative behavior from human partners when full autonomy is not yet possible. For the first phase of our research reported here, wizards play the role of dialogue manager, acting as a robot’s dialogue processing. We describe a novel step within WOz methodology that incorporates two wizards and control sessions: the wizards function much like corpus annotators, being asked to make independent judgments on how the robot should respond when receiving the same verbal commands in separate trials. We show that inter-wizard discussion after the control sessions and the resolution with a reconciled protocol for the follow-on pilot sessions successfully impacts wizard behaviors and significantly aligns their strategies. We conclude that, without control sessions, we would have been unlikely to achieve both the natural diversity of expression that comes with multiple wizards and a better protocol for modeling an automated system.},

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

pubstate = {published},

tppubtype = {inproceedings}

}