The Association for the Advancement of Artificial Intelligence is pleased to present the 2022 Fall Symposium Series, to be held at the Westin Arlington Gateway in Arlington, Virginia, November 17-19.
Symposia generally range from 40–75 participants each. Participation will be open to active participants as well as other interested individuals on a first-come, first-served basis. Each participant will be expected to attend a single symposium.
The program will include the following eight symposia:
- FS-22-01 Artificial Intelligence for Human-Robot Interaction (AI-HRI)
- FS-22-02 Artificial Intelligence for Predictive Maintenance
- FS-22-03 Distributed Teaching Collaboratives for AI and Robotics
- FS-22-04 Knowledge-Guided Machine Learning
- FS-22-05 Lessons Learned for Autonomous Assessment of Machine Abilities (LLAAMA)
- FS-22-07 The Role of AI in Responding to Climate Challenges
- FS-22-08 Thinking Fast and Slow and Other Cognitive Theories in AI
ARTIFICIAL INTELLIGENCE FOR HUMAN-ROBOT INTERACTION (AI-HRI)
The Artificial Intelligence (AI) for Human-Robot Interaction (HRI) Symposium has been a successful venue of discussion and collaboration since 2014. Last year, we reviewed the achievements of the AI-HRI community over the last decade. This year, we are focusing on a visionary theme, exploring the future of AI-HRI. Accordingly, we added a Blue Sky Ideas track to foster a forward-thinking discussion on the future research at the intersection of AI and HRI. As always, we appreciate all contributions related to any topic on AI/HRI and welcome new researchers who wish to take part in this growing community.
With the success of past symposia, AI-HRI impacts a variety of communities and problems, and has pioneered the discussions in recent trends and interests. This year’s AI-HRI Fall Symposium aims to bring together researchers and practitioners from around the globe, representing a number of university, government, and industry laboratories. In doing so, we hope to accelerate research in the field, support technology transition and user adoption, and determine future directions for our group and our research.
- Future of AI-HRI and “Blue Sky” ideas
- Ubiquitous HRI, including AR and VR
- Ethics in HRI
- Trust and explainability in HRI
- Architectures and systems supporting autonomous HRI
- Task learning
- Dialog systems
- Field studies and empirical HRI
- Safety and ergonomics in HRI
- Software tools for autonomous HRI
- Robot planning and decision-making
- Social robots
- Physical HRI
- Knowledge representation and reasoning to support HRI
- HRI in teams and groups
- Replication studies and reproducibility
- Test methods and metrics for AI-HRI
Authors may submit under one of these paper categories:
- Full papers (6-8 pages) highlighting state-of-the-art HRI-oriented AI research, HRI research focusing on the Future of AI-HRI, the use of autonomous AI systems, or the implementation of AI systems in commercial HRI products.
- Short papers (2-4 pages) outlining new or controversial views on AI-HRI research or describing ongoing AI-oriented HRI research.
- Tool papers (2-4 pages) describing novel software, hardware, or datasets of interest to the AI-HRI community.
- Blue Sky papers (2-4 pages) fostering a forward-thinking discussion on the future at the intersection of AI and HRI.
Please see the AAAI Author Kit to ensure that your submission has the proper formatting.
Symposium participants presenting their work are encouraged to include a perspective on the reproducibility and ethics in HRI, though all research on AI-HRI will be considered. Please submit papers here: https://easychair.org/my/conference?conf=fss22<./p>
- Zhao Han (General Co-Chair, Colorado School of Mines, USA)
- Emmanuel Senft (General Co-Chair, University of Wisconsin-Madison, USA)
- Muneeb I. Ahmad (Communication Co-Chair, Swansea University, UK)
- Shelly Bagchi (Communication Co-Chair, National Institute of Standards and Technology, USA)
- Justin W. Hart (University of Texas at Austin, USA)
- Daniel Hernández García (Heriot-Watt University, UK)
- Boyoung Kim (Program Co-Chair, George Mason University, USA)
- Matteo Leonetti (King’s College London, UK)
- Ross Mead (Semio, USA)
- Reuth Mirsky (Bar Ilan University, Israel)
- Ahalya Prabhakar (École Polytechnique Fédérale de Lausanne (EPFL), Switzerland)
- Ruchen Wen (Program Co-Chair, Colorado School of Mines, USA)
- Jason R. Wilson (Diversity & Sponsorship Chair, Franklin & Marshall College, USA)
- Amir Yazdani (Publicity Chair, University of Utah Robotics Center, USA)
- Megan L. Zimmerman (National Institute of Standards and Technology, USA)
For More Information
For more information and updates, please visit the Symposium Website.
ARTIFICIAL INTELLIGENCE FOR PREDICTIVE MAINTENANCE
Complex, physical systems degrade over time, and continued maintenance is required to ensure their peak performance. Upkeep of well-engineered systems is often straightforward, yet significant challenges lie in knowing when and what to maintain. Traditional approaches to managing maintenance activities rely on scheduled or condition-based approaches. Predictive Maintenance (PMx) paradigm complements them with the ability to forecast needs into the future, reducing monetary and logistical burden of ownership, boosting operational safety, and reducing system down-time due to unexpected failures. Past successes show that PMx is capable of achieving those goals, however, there are remaining challenges and untapped opportunities in applying this technology at-scale in real-world settings. Many of these outstanding issues can be resolved with Artificial Intelligence.
This symposium aims to bring together researchers and practitioners across academia, industry, and government in order to discuss challenges, approaches, and needs in the field of AI-driven PMx, as well as fuel collaborative efforts which will accelerate the progress of adopting AI across entire organizations that maintain critical systems.
Topics (but are not limited to)
- Readying maintenance, logistics, and/or systems data for AI
- Analytical frameworks for critical system prognostics
- Impact assessments of AI-PMx
- Implementation strategies
- Using AI to maintain peak operation of cyber-physical systems
The symposium will feature sessions of talks, organized by topic area, to present submitted work. The program will also include keynote speakers, panel discussions, and a poster session.
Unpublished works up to 6 pages in length are eligible for submission. All submissions will be blind reviewed. Please submit papers here: https://easychair.org/my/conference?conf=fss22.
Chair: Dr. Nicholas Gisolfi
Carnegie Mellon University
5000 Forbes Ave, NSH 3115
Pittsburgh, PA 15213, USA
Dr. Artur Dubrawski, Carnegie Mellon University, firstname.lastname@example.org
Dr. Abdel-Moez Bayoumi, University of South Carolina, email@example.com
Mr. David Alvord, Georgia Tech Research Institute, firstname.lastname@example.org
Dr. Stephen Robinson, University of California, email@example.com
Program Committee Co-Chairs
Dr. Artur Dubrawski, Carnegie Mellon University, firstname.lastname@example.org
Dr. Dragos Margineantu, Boeing Research & Technology, email@example.com
For More Information
Please see https://autonlab.github.io/pmx_aaai_fss_2022/
DISTRIBUTED TEACHING COLLABORATIVES FOR AI AND ROBOTICS
The idea of a Distributed Teaching Collaborative is for faculty at multiple universities to collaborate in teaching a course jointly across institutions. The symposium aims to design a Distributed Teaching Collaboratives model to minimize the overhead for faculty to update a current course offering, create a new course, or improve the efficiency of a course to engage in other scholarly activities (such as for research). This model is also intended to provide focused collaborations between R1 universities and HBCUs with clear goals, which can serve as a foundation for larger partnerships and improved pipelines to graduate programs.
The symposium will facilitate community building and discussion through several keynote presentations, lightning talks, and panel discussions by thought leaders across AI, robotics, and education at both R1 institutions and HBCUs. The first day will focus on the general idea of a distributed classroom and why it is important at HBCUs. This will involve talks and a panel on existing distributed teaching collaboratives as well as a panel on teaching challenges at HBCUs. The goal of the first day is to set the tone and expectation of the symposium as well as learn more about the challenges HBCU faculty face teaching new courses. The second day will build on the first day to focus on the curriculum and the student experience. This schedule will include lightning talks, a keynote and panels focused on how best to teach AI courses and the implication of equitable curriculums on the distributive classroom. In addition, we will host a panel discussion with students to get a better sense of the student experience and how they learn and engage with AI curriculum in the classroom.
Submit an abstract (at most one page) describing research related to any of these questions to the Symposium EasyChair site.
Emmanuel Johnson, University of Southern California, firstname.lastname@example.org, Jana Pavlasek, University of Michigan, email@example.com, Odest Chadwicke Jenkins, University of Michigan, firstname.lastname@example.org, Yolanda Gil, University of Southern California, email@example.com
For More Information
Please see https://dtc-ai.github.io/.
KNOWLEDGE-GUIDED MACHINE LEARNING
Knowledge-guided Machine Learning (KGML) is an emerging paradigm of research that aims to integrate scientific knowledge in the design and learning of machine learning (ML) methods to produce ML solutions that are generalizable and scientifically consistent with established theories. KGML is ripe with research opportunities to influence fundamental advances in ML for accelerating scientific discovery and has already begun to gain attention in several branches of science including physics, chemistry, biology, fluid dynamics, and geoscience. The goal of this symposium is to nurture the community of researchers working at the intersection of ML and scientific fields, by providing a common platform to cross-fertilize ideas from diverse fields and shape the vision of the rapidly growing field of KGML.
We encourage participation on topics that explore any form of synergy between scientific principles and artificial intelligence (AI)/ML methods. Examples of relevant submissions include (but are not limited to):
- AI/ML algorithms that employ soft or hard scientific constraints in the learning process.
- Physics-informed neural networks for solving partial differential equations.
- Modeling multi-scale multi-physics phenomena.
- Methods to encode scientific knowledge in AI/ML model architecture.
- Science guided generative or reinforcement learning methods.
- Approaches that use scientific knowledge for interpreting ML results along the lines of explainable AI.
- Surrogate and reduced order modeling methods.
- Parameterization and downscaling methods.
- AI/ML methods for discovering governing equations from data.
- Hybrid constructions of science-based & AI/ML-based models.
- Software development facilitating the inclusion of scientific knowledge in learning
- Inverse modeling & system identification techniques using AI.
- Techniques for using data to calibrate parameters and system states in scientific models.
Our symposium will involve a mix of activities including keynote and invited talks, breakout sessions, panel discussions, and poster sessions.
We are currently accepting paper submissions for position, review, or research articles in two formats: (1) short papers (2-4 pages) and (2) full papers (6-8 pages). All submissions will undergo peer review and authors will have the option to publish their work in an open access proceedings site. Please submit papers to https://easychair.org/conferences/?conf=fss22.
Anuj Karpatne (Virginia Tech; firstname.lastname@example.org), Ramakrishnan Kannan (Oak Ridge National Laboratory; email@example.com), Paris Perdikaris (University of Pennsylvania; firstname.lastname@example.org), Youzuo Lin (Los Alamos National Laboratory; email@example.com), Xiaowei Jia (University of Pittsburgh; firstname.lastname@example.org) and Vipin Kumar (University of Minnesota; email@example.com).
For More Information
Please see https://sites.google.com/vt.edu/kgml-aaai-22.
LESSONS LEARNED FOR AUTONOMOUS ASSESSMENT OF MACHINE ABILITIES (LLAAMA)
Modern civilian and military systems have created a demand for sophisticated intelligent autonomous machines capable of operating in uncertain dynamic environments. Such systems are realizable thanks in large part to major advances in perception and decision-making techniques, which in turn have been propelled forward by modern machine learning tools. However, these newer forms of intelligent autonomy raise questions about when/how communication of the operational intent and assessments of actual vs. supposed capabilities of autonomous agents impact overall performance.
This symposium session will examine possibilities for enabling intelligent autonomous systems to self-assess and communicate their ability to effectively execute assigned tasks, as well as reason about the overall limits of their competencies and maintain operability within those limits. The symposium will bring together researchers working in this burgeoning area of research to share lessons learned, identify major theoretical and practical challenges encountered so far, and potential avenues for future research and real-world applications.
Topics of interest (may include, but are not limited to):
We invite contributions from researchers in AI/expert systems, human factors, autonomous robotics and control/complex systems engineering, and other related disciplines that explore several key areas, including:
- Applications and studies of competency self-assessments in field robotics and other real-world autonomous systems
- Measures of competency for operational self-assessment by autonomous agents
- AI/ML, uncertainty quantification, formal methods, and algorithmic meta-reasoning techniques to enable/support autonomous competency self-assessment
- Presentation and communication of machine generated competency self-assessments to human users/stakeholders
- Techniques for evaluating the quality of machine generated competency self-assessments (e.g. correctness, completeness, fidelity, reliability).
This format of the symposium will include invited talks and contributed paper presentations by leading researchers and technical experts, as well as panel discussions and group breakout sessions focusing on the implementation of competency assessment in real autonomous systems.
Please submit one of the following types of submissions via the AAAI SSS-22 EasyChair site here https://easychair.org/conferences/?conf=fss22.
- Regular papers (6-8 pages + references)
- Position papers (2-4 pages + references)
- Summary of previously published papers (1-2 pages)
Aastha Acharya, Nicholas Conlon, Nisar Ahmed (University of Colorado Boulder); Rebecca Russell, Michael Crystal (Draper); Brett Israelsen (Raytheon Technologies Research Center); Ufuk Topcu (UT Austin); Zhe Xu (Arizona State University); Daniel Szafir (UNC)
For More Information
Please see the symposium website.
Contact: Nisar Ahmed (firstname.lastname@example.org)
THE ROLE OF AI IN RESPONDING TO CLIMATE CHALLENGES
Climate change is one of the most pressing challenges of our time, requiring rapid action across society. As artificial intelligence tools (AI) are rapidly deployed, it is therefore crucial to understand how they will impact climate action. On the one hand, AI can support applications in climate change mitigation (reducing or preventing greenhouse gas emissions), adaptation (preparing for the effects of a changing climate), and climate science. These applications have implications in areas ranging as widely as energy, agriculture, and finance. At the same time, AI is used in many ways that hinder climate action (e.g., by accelerating the use of greenhouse gas-emitting fossil fuels). In addition, AI technologies have a carbon and energy footprint themselves. This symposium seeks participants from across academia, industry, government, and civil society to explore these intersections of AI with climate change, as well as how each of these sectors can contribute to solutions.
Topics (but are not limited to):
- AI applications and methods for climate change mitigation, adaptation, and climate science (across all societal sectors, including agriculture, buildings, heavy industry, power and energy, transportation, and forestry), and supporting societal priorities such as energy and climate security, and climate equity.
- Considerations and frameworks for the development, deployment, and evaluation of AI-based climate solutions (e.g., standards, best practices, ethical frameworks, and mechanisms for stakeholder engagement)
- Mechanisms for developing, deploying, scaling, and evaluating AI-based climate solutions (e.g., funding, data, regulatory frameworks, and public-private partnerships)
- Methodologies and frameworks for assessing the climate impacts of AI technologies in general (e.g., increased computational energy demand, the effects of applications, and broader systemic effects), including strategies for measurement and reporting
- Governance and policies required to align AI with societal climate change goals and the UN Sustainable Development Goals
The symposium will include keynote talks, panels, presentations of contributed work, poster sessions, and discussion sessions.
We are accepting paper submissions for position, review, or research articles in two formats: (a) short papers (2-4 pages) and (b) full papers (6-8 pages). All submissions will undergo peer review and authors will have the option to publish their work in an open access proceedings site. Papers can be submitted through the AAAI Fall 2022 Symposia EasyChair site here https://easychair.org/conferences/?conf=fss22, selecting the appropriate track and theme. Submissions due 29 July, 2022.
Feras A. Batarseh (Virginia Tech), Priya L. Donti (Carnegie Mellon), Ján Drgoňa (PNNL), Kristen Fletcher (Naval Postgraduate School), Pierre-Adrien Hanania (Capgemini), Melissa Hatton (Capgemini Government Solutions), Srinivasan Keshav (University of Cambridge), Bran Knowles (Lancaster University), Raphaela Kotsch (University of Zurich), Sean McGinnis (Virginia Tech), Peetak Mitra (PARC), Alex Philp (Mitre), Jim Spohrer (ISSIP), Frank Stein (Virginia Tech), Meghna Tare (UT Arlington), Svitlana Volkov (PNNL), Gege Wen (Stanford)
For More Information
THINKING FAST AND SLOW AND OTHER COGNITIVE THEORIES IN AI
This AAAI fall symposium aims to be a meeting point for researchers of various disciplines investigating or developing frameworks inspired from the two-system theory of D. Kahneman or other cognitive theories of human decision making to advance AI.
- System 1 and system 2 capabilities in AI and their dynamic refinement over time
- Governance and coordination between AI system 1 and system 2
- Introspection and meta-cognition in agents ‘capabilities
- General machine decision frameworks vs specific solutions
- Leveraging cognitive theories of human reasoning to advance AI
- The role of machine learning and reasoning, and their combination, in ethical machine decision making
Given the multidisciplinary nature of the topic, the symposium encourages cross-fertilization among experts of various disciplines inside and outside AI. The program includesinvited talks, short presentations of accepted papers, and panel discussions where panelists and participants will discuss specific topics in depth. Moreover, we plan to organize poster sessions to facilitate discussions between the authors and the other participants.
We encourage academics, policy makers, and practitioners in the many disciplines relevant for the topics of this event to join this Symposium. Anyone interested must submit a max 2 page expression of their interest in the topic. Additionally, interested researchers that want to present their recent work should submit a paper of at most 8 pages, references included. Submissions can be technical papers, descriptions of use cases, lessons learnt, datasets or other resources.
The submission format is the standard double-column AAAI Proceedings Style, which can be found at https://www.aaai.org/Publications/Templates/AuthorKit22. zip. The review process is single-blind. Submissions of both expressions of interest and papers should be done through this link https://easychair.org/conferences/?conf=fss22.
We plan to publish a collection of contributions after the Symposium including selected work from the event and possibly other contributions (another call will circulate later).
- Abstracts/Papers due: August 28, 2022
- Author Notification: September 16, 2022
- Symposium: November 17-19, 2022
Marianna Ganapini, Union College, email@example.com, Lior Horesh, IBM Research, firstname.lastname@example.org, Andrea Loreggia, University of Brescia, email@example.com, Nicholas Mattei, Tulane University, firstname.lastname@example.org, Francesca Rossi, IBM Research, email@example.com, Biplav Srivastava, University of South Carolina, firstname.lastname@example.org, Brent Venable, University of South Florida and IHMC, email@example.com
For More Information
As all AAAI Symposia 2022, this is an in-person event.
Contact: Andrea Loreggia (firstname.lastname@example.org)