no code implementations • 1 Apr 2024 • Casey Kennington, Malihe Alikhani, Heather Pon-Barry, Katherine Atwell, Yonatan Bisk, Daniel Fried, Felix Gervits, Zhao Han, Mert Inan, Michael Johnston, Raj Korpan, Diane Litman, Matthew Marge, Cynthia Matuszek, Ross Mead, Shiwali Mohan, Raymond Mooney, Natalie Parde, Jivko Sinapov, Angela Stewart, Matthew Stone, Stefanie Tellex, Tom Williams
The ability to interact with machines using natural human language is becoming not just commonplace, but expected.
1 code implementation • 13 Jun 2023 • Tathagata Chakraborti, Jungkoo Kang, Christian Muise, Sarath Sreedharan, Michael Walker, Daniel Szafir, Tom Williams
This paper describes TOBY, a visualization tool that helps a user explore the contents of an academic survey paper.
no code implementations • 17 Mar 2022 • Zhao Han, Boyoung Kim, Holly A. Yanco, Tom Williams
Autonomous robots must communicate about their decisions to gain trust and acceptance.
no code implementations • 16 Mar 2022 • Zhao Han, Tom Williams
Within the human-robot interaction (HRI) community, many researchers have focused on the careful design of human-subjects studies.
2 code implementations • 23 Feb 2022 • Michael Walker, Thao Phung, Tathagata Chakraborti, Tom Williams, Daniel Szafir
Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI) has been gaining considerable attention in research in recent years.
no code implementations • 17 Jul 2020 • Poulomi Pal, Tom Williams
Language-capable interactive robots participating in dialogues with human interlocutors must be able to naturally and efficiently communicate about the entities in their environment.
no code implementations • 16 Jul 2020 • Ryan Blake Jackson, Tom Williams
We implement our solution in the DIARC robot architecture, which, to our knowledge, is the only current robot architecture with both moral reasoning and clarification request generation capabilities.
no code implementations • 16 Jul 2020 • Tom Williams, Torin Johnson, Will Culpepper, Kellyn Larson
To engage in human-like dialogue, robots require the ability to describe the objects, locations, and people in their environment, a capability known as "Referring Expression Generation."
no code implementations • 22 May 2020 • Poulomi Pal, Lixiao Zhu, Andrea Golden-Lasher, Akshay Swaminathan, Tom Williams
We present and compare two such models of cognitive status: a rule-based Finite State Machine model directly informed by the GH literature and a Cognitive Status Filter designed to more flexibly handle uncertainty.
no code implementations • 11 Sep 2019 • Justin W. Hart, Nick DePalma, Richard G. Freedman, Luca Iocchi, Matteo Leonetti, Katrin Lohan, Ross Mead, Emmanuel Senft, Jivko Sinapov, Elin A. Topp, Tom Williams
The past few years have seen rapid progress in the development of service robots.
Robotics
no code implementations • 26 Nov 2018 • Tom Williams, Ravenna Thielstrom, Evan Krause, Bradley Oosterveld, Matthias Scheutz
In previous work, we developed a Consultant Framework that facilitates modality-agnostic access to information distributed across a set of heterogeneously represented knowledge sources.
no code implementations • 6 Jul 2018 • Daniel Kasenberg, Vasanth Sarathy, Thomas Arnold, Matthias Scheutz, Tom Williams
In this paper we describe moral quasi-dilemmas (MQDs): situations similar to moral dilemmas, but in which an agent is unsure whether exploring the plan space or the world may reveal a course of action that satisfies all moral requirements.
no code implementations • WS 2017 • Tom Williams, Matthias Scheutz
For situated agents to effectively engage in natural-language interactions with humans, they must be able to refer to entities such as people, locations, and objects.
no code implementations • 1 Feb 2017 • Eric Eaton, Sven Koenig, Claudia Schulz, Francesco Maurelli, John Lee, Joshua Eckroth, Mark Crowley, Richard G. Freedman, Rogelio E. Cardona-Rivera, Tiago Machado, Tom Williams
The 7th Symposium on Educational Advances in Artificial Intelligence (EAAI'17, co-chaired by Sven Koenig and Eric Eaton) launched the EAAI New and Future AI Educator Program to support the training of early-career university faculty, secondary school faculty, and future educators (PhD candidates or postdocs who intend a career in academia).