2026 Fall Symposium Series

Sponsored by the Association for the Advancement of Artificial Intelligence 

Symposia generally range from 40–75 participants each. Participation was open to active participants as well as other interested individuals on a first-come, first-served basis. Each participant is expected to attend a single symposium. 

The program will host the following symposia:

• Aligning with Whom? Pluriversal and Paraconsistent Frameworks for Value Alignment in AI Systems
• METACOG-26: Symposium on Artificial Metacognition
• The 3rd Symposium on AI Trustworthiness and Risk Assessment for Challenged Contexts (ATRACC)
• Planning for a Better Planet
• Parsimonious Scientific Machine Learning: Accelerating Scientific Discovery with AI under Data and Compute Constraints
• Second AAAI Symposium on Quantum Information & Machine Learning (QIML): Bridging Quantum Computing and Artificial Intelligence
• Unifying Representations for Robot Application Development
• Trustworthy Agentic Systems
• Agentic and Trustworthy AI for Health and the Global AI-Ready Nursing Workforce (AT-AI4H-NW 2026)

AAAI Code of Conduct for Events and Conferences 

All persons, organizations and entities that attend AAAI conferences and events are subject to the standards of conduct set forth on the AAAI Code of Conduct for Events and Conferences. 

Registration and General Information 

Registration Fees

The conference registration fee includes admission to one symposium, access to the electronic proceedings, coffee breaks, and the opening reception.

Refund Requests

The deadline for refund requests is October 2, 2026. All refund requests must be made in writing to fssreg@aaai.org. A $50.00 processing fee will be assessed for all refunds.

Registration Fee Schedule

AAAI Silver Registration

(Includes AAAI membership, plus the conference)

Visa Information 

Letters of invitation can be requested by accepted FSS-26 authors or registrants with a completed registration with payment. You can access the visa letter form via the link in your registration confirmation email.

Hotel Information 

For your convenience, AAAI has reserved a block of rooms at the Westin Arlington. The Westin Arlington is located in the Ballston area of Arlington. It is a short walk from the Ballston Metro Station, which allows guests to easily explore Arlington, downtown Washington, DC, Alexandria, or Georgetown. Reagan National Airport is easily accessible via the Washington Metro rapid transit.

The conference room rate per night is $249.00 (Single/Double).  

Rates do not include applicable state and local taxes (approximately 14.25%), or hotel fees in effect at the time of the meeting. Symposium attendees must contact the Westin Arlington Gateway directly. Please request the group rate for the Association for the Advancement of Artificial Intelligence (AAAI) when reserving your room. The cutoff date to reserve your room at this discounted rate is October 21, 2026. Reservations after this date will be accepted based on availability at the hotel’s prevailing rate. All reservations must be secured by one night’s deposit per room, via credit card. Reservations may be cancelled with no penalty up to 5:00 pm, 72 hours prior to the date of arrival. After that time, a penalty of one night’s room and tax will be incurred. Upon check-in, date of departure must be confirmed. Early departure will result in a fee equal to one night’s guest room rate.  

Westin Arlington
801 North Glebe Road,
Arlington, Virginia 22203 USA 

Transportation to the Hotel 

For complete transportation information and directions, please see 
https://www.marriott.com/hotels/maps/travel/wasag-the-westin-arlington-gateway/ and scroll down to “Getting Here.” 
Hotel Parking: On-site parking fee is 40 USD daily 

Disclaimer 
In offering the Westin Arlington (hereinafter referred to as “Supplier”), and all other service providers for the AAAI Fall Symposium Series, the Association for the Advancement of Artificial Intelligence acts only in the capacity of agent for the Supplier, which is the provider of hotel rooms and transportation. Because the Association for the Advancement of Artificial Intelligence has no control over the personnel, equipment or operations of providers of accommodations or other services included as part of the Symposium program, AAAI assumes no responsibility for and will not be liable for any personal delay, inconveniences or other damage suffered by symposium participants which may arise by reason of (1) any wrongful or negligent acts or omissions on the part of any Supplier or its employees, (2) any defect in or failure of any vehicle, equipment or instrumentality owned, operated or otherwise used by any Supplier, or (3) any wrongful or negligent acts or omissions on the part of any other party not under the control, direct or otherwise, of AAAI. 

Onsite Registration Schedule 

Upon arrival please check in at the registration area for your badge. AAAI will release the exact location of registration closer to the event.

Registration hours will be: 

General Event Schedule 
Each Symposium schedule may vary slightly

Additional Information: 

General inquiries regarding the symposium series should be directed to AAAI at fssreg@aaai.org. 

Aligning with Whom? Pluriversal and Paraconsistent Frameworks for Value Alignment in AI Systems

Description of Symposium (objectives): Current alignment methods (RLHF, Constitutional AI, DPO) treat alignment as aggregating diverse human values into a single utility function fixed at training time. When principles are elicited from pluralistic populations, structurally irreducible contradictions emerge and are resolved, in deployment, by majority vote, which silences minorities, indigenous epistemologies, and Global South value frameworks. This symposium asks a question that is political and epistemic before it is technical: aligning with whom? It convenes researchers from formal logic, AI safety, cross-cultural value elicitation, and Global South AI governance to ask whether current architectures can handle genuine value pluralism and what alternatives exist. The working thesis is that paraconsistent, three-component belief frameworks (Belnap-Dunn logic, Dempster-Shafer belief functions, imprecise probability) can represent irreducible value contradictions without collapsing them into a single ordering.

Topics: Formal frameworks for value indeterminacy; the majority-vote problem in Constitutional AI; cross-cultural value elicitation (Pol.is, deliberative mini-publics); Global South and decolonial perspectives on alignment; culturally sensitive evaluation benchmarks; uncertainty quantification in LLMs; pedagogy of indeterminacy.

Format of Symposium: A 2.5-day program combining short-paper presentations (25 min + 10 min Q&A), two keynote-anchored panels (formal frameworks; cross-cultural empirics), three parallel working groups producing one-page position statements, and a closing synthesis plenary toward a shared research agenda. Target size: 45-60 participants.

Attendance: Open to accepted authors, invited speakers, committee members, and interested attendees; roughly 20% of slots are reserved for non-presenting participants from outside the organizing communities. No prior affiliation is required.

Submission requirements: Full papers of 6-8 pages or short/position papers and extended abstracts of 2-4 pages, in AAAI format; both mature results and work in progress are welcome. At least one author per accepted paper must attend and present in person.

Submission Site Information: https://easychair.org/conferences/?conf=fss26

Symposium Committee: Maikel Leyva-Vázquez (Chair), Universidad Bolivariana del Ecuador / Universidad de Guayaquil, Ecuador, mleyvaz@gmail.com; Florentin Smarandache (Co-Chair), University of New Mexico, Gallup, USA, smarand@unm.edu. Additional committee members and invited speakers will be announced.

Symposium External URL: https://www.google.com/url?q=https://mleyvaz.github.io/pluriversal-alignment-aaai2026&source=gmail&ust=1783466987252000&sa=E

METACOG-26: Symposium on Artificial Metacognition

Artificial metacognition is the ability of an AI-enabled system to reason about itself. Rooted in cognitive psychology and revisited in early-2000s AAAI symposia, the topic has re-emerged with large language models, neurosymbolic AI, and the demand for robust, secure, and resource-aware systems. Going beyond uncertainty quantification and out-of-distribution detection, metacognition addresses error detection together with security, efficiency, explainability, and corrective action in a unified, often cognitively inspired framework. Building on METACOG-23, METACOG-25, and a forthcoming IEEE Intelligent Systems special issue, METACOG-26 aims to survey current approaches to monitoring, control, and metareasoning; clarify their requirements and trade-offs; identify methods that improve performance in operational and cross-domain settings; map promising application areas; foster collaboration across AI, cognitive science, control, and systems engineering; and examine trust, calibration, and human-AI teaming.

Topics

AI agents with metacognition; cognitive architectures with metacognitive extensions (ACT-R, Soar, the Common Model of Cognition); metacognitive rule learning; critique models; explainable performance prediction of black-box systems; stress testing of reinforcement-learning and perception systems; metacognitive monitoring versus control; neuro-symbolic architectures; self-adaptive and self-healing systems; out-of-distribution detection and correction; trust calibration and human-in-the-loop metacognition; benchmarks and evaluation methodology; and applications to autonomy, robotics, cyber, and decision support.

Format

A 2.5-day, fully in-person symposium following the standard AAAI Fall Symposium structure, combining peer-reviewed oral sessions, extended discussion, a poster session, three invited keynotes, and a closing panel. Confirmed keynotes are John Laird (University of Michigan), Mark Steyvers (UC Irvine), and Susmit Jha (DARPA). Accepted papers will appear in the AAAI Technical Report series.

Attendance

Open to accepted authors and other interested researchers registering through AAAI; the two prior workshops drew participants from more than 35 institutions across academia, government, and industry worldwide, and space is limited, so early registration is encouraged.

Submission Requirements

Full papers (up to 8 pages) for oral presentation and short papers or extended abstracts (up to 4 pages) for posters, in AAAI two-column format, peer-reviewed by the organizing and program committees.

• Submit papers by September 1, 2026 via https://easychair.org/conferences/?conf=fss26

Symposium Committee

• Paulo Shakarian (Lead), Syracuse University, pashakar@syr.edu
• Nathaniel D. Bastian, DARPA, nathaniel.bastian@darpa.mil
• Francesco Restuccia, Northeastern University, f.restuccia@northeastern.edu
• Christian Lebiere, Carnegie Mellon University, cl@cmu.edu
• Arslan Basharat, Kitware, Inc., arslan.basharat@kitware.com
• Andrea Stocco, University of Washington, stocco@uw.edu

AI Trustworthiness and Risk Assessment for Challenged Contexts (ATRACC-26)

The global focus of this symposium track is trustworthy, safe, and assured AI across the full AI system lifecycle. AI assurance requires developing approaches for assuring individual AI components as well as approaches that consider the larger sociotechnical systems in which they are embedded. This symposium is interested in work on any component related to AI assurance. The track emphasizes methods that provide evidence, metrics, and bounds for fairness, reliability, robustness, security, privacy, reproducibility, accountability, transparency, and resilience in the context of quantifying and managing AI-system risk. It spans foundational research, metrology, formal methods, systems engineering, software engineering, human factors, policy, governance, and operational practice. The symposium will bring together researchers and practitioners from academia, industry, government, standards organizations, and civil society who are invested in addressing the scientific and engineering challenges of AI assurance in applications where a priori understanding of risk is critical.

Topics of interest include, but are not limited to:

• Trustworthy multi-agent, agentic AI, and embodied AI (robotic) systems, with emphasis on governance, alignment, robustness, reliability, controllability, accountability, delegation, and emergent behavior in risk-averse contexts.
• Sociotechnical perspectives on AI-enabled systems, including the relationship among technical performance, human trust, organizational accountability, governance, standards, metrology, and social-science and humanities methods.
• Approaches for enhancing reasoning in large language, foundation, and multimodal models, including causal reasoning, neuro-symbolic methods, retrieval-augmented generation, formal reasoning, outcome verification, and process supervision.
• Verification, validation, and testing of AI systems, including quantitative AI and system performance indicators, operational design domain specification, confidence bounds, and links among performance, trustworthiness, and trust.
• Evaluation of AI system vulnerabilities, risks, and impacts, including prompt injection, data poisoning, model extraction, tool misuse, adversarial manipulation, automated red teaming, degradation objectives, and liability assessment.
• Governance, standards, policy, and regulatory approaches for trustworthy AI, including AI management systems, conformity assessment, risk classification, independent evaluation, and cross-border interoperability.
• Neuro-symbolic, causal, and knowledge-based methods that combine data-driven learning with domain knowledge to support reliability requirements, quantify uncertainty, reduce overgeneralization, and improve the trustworthiness of AI-enabled critical applications.

Symposium Details:

• Duration: 2 1/2 days
• Features: Keynote and invited talks from accomplished experts in the field of Trustworthy AI, panel sessions, presentation of selected papers, student papers, and a poster session.

Submission Details:

• Full papers: Maximum 8 pages
• Poster/short/position papers: Maximum 4 pages
• Deadline for submission: September 1
• Notification of acceptance or rejection: September 15
• Camera-ready papers for symposium proceedings: September 29
• Submission Link: https://easychair.org/conferences/?conf=fss26

All accepted papers will be included in the AAAI Fall 2026 proceedings.

For a provisional schedule, program committee, and practical information, please visit the Symposium Website.

Program Chairs:

Brian Hu, Kitware, brian.hu@kitware.com

Biplav Srivasta, University of South Carolina, biplav.s@sc.edu

James (Xi) Zheng, Macquarie University, jameszhengxi@utexas.edu

Naman Goel, University of Oxford, Alan Turing Institute, naman.goel@cs.ox.ac.uk

Martin Gonzalez, IRT System X, martin.gonzalez@irt-systemx.fr

Program Committee:

Bertrand Braunschweig, Confiance.ai, bertrand.braunschweig@irt-systemx.fr

Maximilian Poretschkin, Fraunhofer IAIS, Maximilian.Poretschkin@iais.fraunhofer.de

Faïcel Chamroukhi, DS&AI, IRT SystemX, faicel.chamroukhi@irt-systemx.fr

Shoaib Ehsan, University of Southampton, S.Ehsan@soton.ac.uk

Juliette Mattioli, Thales, juliette.mattioli@thalesgroup.com

André Meyer-Vitali, DFKI, andre.meyer-vitali@dfki.de

Foutse Khomh, Polytechnique Montréal and Mila, foutse.khomh@polymtl.ca

Stephen Russell, UWF, russell@uwf.edu

Ariel Kapusta, The MITRE Corporation, akapusta@mitre.org

Planning for a Better Planet

Climate change demands fundamentally new approaches to decision-making. Where to invest scarce adaptation resources across competing priorities, how to design carbon markets that balance efficiency with equity, and how to coordinate across jurisdictions with vastly different historical responsibilities – these are, at their core, problems of reasoning under uncertainty, constrained optimization, and multi-agent coordination.
This symposium aims to bring together the AI and climate communities to discuss opportunities for climate-motivated planning research to make the planet a healthier, more sustainable place. This includes not only AI researchers sharing new algorithms and applications but also climate experts sharing successes and challenges from their own domains, with the goal of highlighting “problems in search of solutions” to drive future climate-oriented planning research.

The 2026 Planning for a Better Planet AAAI Fall Symposium aims to bring together scientists and researchers to discuss autonomous agents that collect information, make decisions, and take actions to help make the planet a healthier, more sustainable place. This goal encompasses a wide array of AI communities, including automated planning and scheduling, multiagent coordination, mechanism design and negotiation protocols, probabilistic planning under uncertainty, robotics motion and path planning, operations research, and optimization, among others. We hope to gather members of these automated planning and reasoning communities alongside climate advocates, workers, and scientists to share and discuss climate-motivated planning research. Climate domains include, but are not limited to, work that helps us better understand the changing climate, adapt to climate change, and optimize systems, resource distribution, and infrastructure to mitigate long-term climate impacts; and work plans for the socio-political and economic challenges of the transition to net zero.

The climate crisis is one of the most pressing challenges of our time, one whose solution will require focused attention across many domains. This symposium aims to bring together researchers from climate-related and AI-related fields to achieve the following objectives:

1. Identify the broad planning/reasoning research challenges and opportunities posed by the climate crisis for AI and robotics.
2. Articulate broad research themes and directions within automated planning, reasoning, and decision-making that support climate science, climate mitigation, climate adaptation, and climate justice — including not only climate-related applications of planning but also new areas of planning research inspired by climate applications.
3. Surface opportunities in shaping the future of the climate through how decisions to act are made—including, e.g., planning sensor deployment to learn more effective models, planning more equitable, sustainable allocation of resources, planning for disaster recovery and risk-aware planning, and using planning to translate the proliferation of data and models into action that is just and sustainable.
4. Cultivate a network and community for researchers working at the intersection of AI/robotics and climate that will facilitate climate advocacy within planning research and launch new collaborations and research directions
5. For climate domain experts, this symposium will offer direct exposure to formal planning and reasoning tools that address problems at the core of climate decision-making: sequential decisions under uncertainty, multi-stakeholder coordination, constrained resource allocation, and risk-aware policy design.

We invite submissions and participants from across the planning and climate domains who are interested in or have engaged in (1) climate-inspired planning research, (2) climate applications of planning methods, and (3) identifying planning/decision-making challenges within climate domains.  We invite both short and long papers across various levels of maturity, including mature solutions, works in progress, and more speculative or prospective work that identifies aspirational future directions for climate-motivated AI planning. We specifically invite submissions from participants in climate domains, highlighting challenges that represent novel opportunities for collaboration with the planning research community. We welcome participation from climate scientists, environmental economists, policy researchers, and practitioners working on a variety of topics including (but not limited to): carbon pricing and market mechanisms, national climate strategy development, adaptation investment planning, energy transition pathways, climate risk assessment, and environmental justice.

Submission Site: https://easychair.org/conferences/?conf=fss26

Parsimonious Scientific Machine Learning: Accelerating Scientific Discovery with AI under Data and Compute Constraints

Scientific discovery is increasingly limited by computational, data, and energy costs preventing the exploration of complex scientific systems at scale. While modern AI excels in data-rich settings, fields such as climate modeling, fluid dynamics, materials discovery, nuclear fusion, subsurface science, and biology face challenges like time-consuming simulations, sparse observations, costly experiments, making efficient learning solutions essential. To address these challenges, Parsimonious Scientific Machine Learning (Parsimonious SciML) is an emerging field of research focused on techniques for sample efficient and compute efficient AI solutions, that are scientifically grounded, explainable, and likely to generalize to out-of-distribution samples, even with limited training data or limited compute budgets. The goal of this symposium is to nurture the cross-disciplinary community of researchers working at the intersection of AI and science by providing a common platform to catalyze and cross-fertilize ideas from diverse fields and shape the vision of the rapidly growing field of Parsimonious SciML.

Topics: We encourage participation on a range of topics exploring the synergy between scientific applications and ML, including (but not limited to): (a) any scientific machine learning (SciML) applications in forward or hybrid modeling with a focus on training under data paucity or compute paucity (b) SciML applications focused on inverse modeling and equation discovery (c) use of scientific knowledge as loss functions or hard constraints in the training of ML models for supervised, unsupervised, and semi-supervised applications, (d) techniques to augment imperfections or infer parameters in science-based models using ML, and (e) use of scientific knowledge in the design, pretraining, finetuning of Foundation models in science. (d) LLMs for accelerating scientific discovery.

Format: Our 2.5 day symposium will include a mix of activities to support education, collaboration, and outreach in the field of Parsimonious SciML, including invited talks, lecture-style tutorials, hands-on demos, panel discussions, poster sessions, and networking/mentoring events.

Submission Requirements: We are accepting short papers (2 pages excluding references) as extended abstracts, and full papers (6 pages excluding references) submissions in a variety of tracks such as: (a) Blue Sky Ideas papers providing a position or perspective of a research area in Parsimonious SciML, (b) Tutorials (lecture-style or hands-on demos) of Parsimonious SciML topics, (c) Posters showing preliminary or full-scale results on cutting-edge research problems, (d) Dataset, Benchmark papers relevant to Parsimonious SciML, (e) early career lightning talks promoting next-generation leaders in Parsimonious SciML including postdocs, early career investigators, and (f) dissertation forum submissions for graduate students to present their dissertation research in SciML. All submissions will undergo review to assess suitability for the symposium. Accepted papers will appear in AAAI proceedings.

Symposium-Website: https://sites.google.com/view/parsciml-fss26/

Paper Submission on Easychair: https://easychair.org/conferences/?conf=fss26

Sympoisum-Chair: Nikhil Muralidhar, Stevens Institute of Technology (nmurali1@stevens.edu)

Organizing-Committee:  Taniya Kapoor, Wageningen University & Research (taniya.kapoor@wur.nl), Bharat Srikishan, Stevens Institute of Technology (bsrikish@stevens.edu) , Anuj Karpatne, VT (karpatne@vt.edu)

Steering-Committee:  Ramakrishnan Kannan, ORNL (kannanr@ornl.gov) , Vipin Kumar, UMN (kumar001@umn.edu)

Second AAAI Symposium on Quantum Information & Machine Learning (QIML): Bridging Quantum Computing and Artificial Intelligence

USAWe invite contributions that explore the intersection of quantum computing and machine learning, including but not limited to:

Quantum Information and Machine Learning (QIML) is an emerging field that leverages quantum algorithms to potentially revolutionize AI paradigms. The goal of this Fall Symposium is to bring together participants from academia, industry, and government to explore how QIML, along with classical ML and High Performance Computing (HPC) research, is currently being expanded to support various fields in science and engineering. Recent advancements in quantum hardware and quantum algorithms have enabled promising applications in classification, regression, generative modeling, optimization, and reinforcement learning. The AAAI Fall 2026 symposium will bring together leading researchers from academia, industry practitioners, and emerging scholars to explore cutting-edge theoretical advancements, novel methodologies, and practical applications, as well as to discuss current challenges. The event will foster interdisciplinary dialogue to highlight both visionary and preliminary work and to identify key challenges and opportunities posed by quantum technologies. The symposium will be organized around several topics within QIML, which include, but are not limited to: 

Areas of Interest

• Quantum algorithms for classical AI tasks
• Quantum-inspired machine learning techniques (QML)
• Quantum Reinforcement Learning
• Quantum Neural Networks & Graph Neural Networks
• Quantum Generative Models
• Variational Quantum Classifiers and Regressors

• Quantum data encoding methods
• Classical AI/ML for quantum systems
• Scalability and hardware considerations
• Applications of QC and QML
• Hybrid Quantum-Classical (QC+HPC) Computing
• Ethical considerations in QC and QML

Topics:

• Quantum algorithms for classical AI (classification, regression, clustering, PCA, etc.)
• Quantum-inspired machine learning techniques
• Quantum Reinforcement Learning
• Quantum Neural Networks
• Quantum Graph Neural Networks
• Quantum Generative Models
• Variational Quantum Classifiers and Regressors
• Quantum data encoding methods
• Classical AI/ML for developing quantum algorithms, quantum security, networking etc.
• Scalability and hardware considerations for QIML applications.
• Applications of QIML to finance, healthcare, climate, security, biomedical, etc.
• Hybrid Quantum Computing with classical High Performance Computing.
• Ethical considerations in QIML

Format of Symposium: 

Two and a half days of invited and contributed presentations, plus panel discussions with industry, government, and academic experts. The symposium will also include an evening poster session.

Attendance:

• Keynote addresses from prominent researchers
• Technical paper presentations
• Panel discussions featuring industry, academia, and government experts
• Breakout groups for focused discussions on emerging QIML topics
• Poster sessions highlighting ongoing research
• Interactive tutorials demonstrating quantum machine learning applications ( i.e., Financial, Supply Chain, and NASA Earth Observational satellite datasets used in implementing QML models),  use of quantum computing platforms, open source libraries, tools, and educational material (e.g., IBM Qiskit Machine Learning Ecosystem, Pennylane, Google Cirq, etc.)

Submissions:

We are accepting paper submissions for position, poster, artifacts, 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.

Submission Requirements: All submissions must follow the AAAI author kit instructions and be submitted through the official AAAI EasyChair site. The review is single-blinded. Accepted submissions will be published in the AAAI symposium proceedings.

• Full papers (6-8 pages): describe work related to the topics above.
• Position Papers (2-4 pages excluding references): discuss topics outlined above.
• Artifact Papers (2-4 pages excluding references): describe artifacts (e.g., software tools or libraries) related to the topics above.
• Poster papers (2 to 4 pages).

Submissions Due to Organizers via the AAAI provided EasyChair site: September 1, 2026
Notifications Sent to Authors (by organizers): September 15th, 2026
Camera Ready Paper Deadline: September 29th, 2026

EasyChair Submission site information available at: https://easychair.org/conferences/?conf=fss26

Symposium Committee

Steering Committee:

• Dr. James Hendler, Rensselaer Polytechnic Institute (RPI), USA.  hendler@cs.rpi.edu
• Dr. Malik Magdon-Ismail, Rensselaer Polytechnic Institute (RPI), USA. magdon@cs.rpi.edu
• Dr. Jennifer Wei, NASA, USA. jennifer.c.wei@nasa.gov
• Dr. Mark Neubauer, University of Illinois Urbana-Champaign,  msn@illinois.edu 

Organizing Committee:

• General Chair: Dr. James Hendler (RPI), USA

Program Chairs:

• Dr. Thilanka Munasinghe, Rensselaer Polytechnic Institute (RPI), USA. munast@rpi.edu
• Dr. Kimberly Cornell, University at Albany, USA. kacornell@albany.edu
• Dr. Malik Magdon-Ismail, Rensselaer Polytechnic Institute (RPI), USA. magdon@cs.rpi.edu

Track Chairs:

• Dr. Samuel Yen-Chi Chen , Wells Fargo, (QML+Aplications), ycchen1989@gmail.com
• Dr. Walter Krawec, University of Connecticut, (Quantum Information Science, Cryptography/Security), walter.krawec@uconn.edu
• Dr. Mark Neubauer, University of Illinois Urbana-Champaign, (High Energy Physics), msn@illinois.edu 

Program Committee:

• Dr. Yulia Gel, Virginia Tech, USA
• Dr. Youzhou Chen, University of California, Riverside, USA
• Dr. Kyo Lee, Jet Propulsion Laboratory – NASA /  Caltech, USA
• Xiaohua, Pan, Goddard Space Flight Center, USA
• Dr. Nick LaHaye, Spatial Informatics Group LLC, USA

Dr. Nistha Langer, Rensselaer Polytechnic Institute (RPI), USA 

• Dr. Phung Lai, University at Albany, USA
• Dr. Jason Kuruzovich, Rensselaer Polytechnic Institute (RPI), USA 
• Dr. Nishtha Langer, Rensselaer Polytechnic Institute (RPI), USA 
• Dr. Sebastian Souyris, Rensselaer Polytechnic Institute (RPI), USA 
• Dr. Gideon Gogovi, Lehigh University, USA

Publicity and Local Arrangements Chair:

• Dr. Thilanka Munasinghe (munast@rpi.edu)

QIML Symposium website external URL:

https://aaai-qiml.github.io

Registration & Hotel information available on the official AAAI Fall26 Symposium website.

Important Registration Details: Please see the official AAAI Fall26 Symposium website for more details, and the QIML Symposium website

AAAI opened registration for the Fall Symposium Series 

October 2, 2026: Deadline for Registration Refund Requests – Last day before Late Registration Rate begins

The Fall Symposium Series is an in-person event:

• All presentations must be delivered in person
• At least one author per accepted paper should attend and present

Unifying Representations for Robot Application Development (UR-RAD)

Overview

Representations that capture tasks, environments, interactions, and knowledge artifacts are central to robotics and artificial intelligence. Researchers have developed a variety of representational approaches, including planning languages, temporal logics, symbolic and neurosymbolic formalisms, learned embeddings, and others, which have enabled robots to act and interact usefully. As embodied systems become increasingly capable, there is a need to understand how these representational approaches relate to one another and how they can be combined to support robust robot behavior.

The 4th Symposium on Unifying Representations for Robot Application Development (UR-RAD) will bring together researchers from robotics, artificial intelligence, cognitive systems, and related fields to explore the representations that underlie robot action and interaction. We welcome perspectives spanning artificial intelligence (AI), neurosymbolic methods, language-based approaches, and hybrid systems. This year’s symposium places particular emphasis on fostering connections across career stages, creating opportunities for junior researchers to engage with the community, discuss emerging ideas and in-progress work, and develop collaborations.

The symposium will also offer mentoring connections for early career researchers. Paper authors and junior members of the community who opt in will have the opportunity for extensive interaction with mentors, who will also guide discussions about individual papers.

Topics

• Representational trends
• Natural language in robotics
• Novel representations & uses
• AI planning for robotics
• Formal methods in robotics
• Representations for robot learning
• Representations for UI
• Robot EUD and programming
• Robot runtime/control environments
• Opportunities for standardization
• Frameworks (e.g., ROS or middleware)
• Open-source & collaboration initiatives
• Identifying representation requirements
• Neurosymbolic representations

Format

The symposium will feature invited speakers, paper presentations, panels, and breakout discussions. To encourage interaction, accepted papers will be presented round-robin style to smaller groups of attendees who will be able to engage in discussions and feedback.

Submission Requirements

We invite the following contributions, anonymized for submission, formatted using the AAAI-26 author kit:

• Full Papers (5-8 pages):Novel research, artifact submissions, or strong works in progress.
• Short Papers (2-4 pages):Positions, smaller artifacts, and early works in progress.

Submission Dates

• Submission deadline: August 24, 2026
• Notification date: September 14, 2026
• Camera Ready Deadline: September 29, 2026

Authors are encouraged to submit earlier rather than later. Submissions should be anonymized for double-blind review.

Submissions should be made through EasyChair: [https://easychair.org/conferences/?conf=fss26]

Organizing Committee

• David Porfirio (George Mason University)
• Saad Elbeleidy (Peerbots)
• Laura M. Hiatt (Naval Research Laboratory)
• Cynthia Matuszek (University of Maryland, Baltimore County)
• Ross Mead (Semio)
• Andrew Schoen (Semio)
• Laura Stegner (George Washington University)
• Ruchen Wen (Colgate University)

Website and Contact Information

Contact: urrad.symposium@gmail.com

Website: ur-rad.github.io

Trustworthy Agentic Systems (TAS 2026)

Agentic systems, in which large language models (LLMs) plan, invoke tools, retrieve external knowledge, and act autonomously over multiple steps, are moving from research prototypes into consequential, real-world decision making. Our ability to guarantee that these systems are reliable, private, safe, and accountable has not kept pace. The Symposium on Trustworthy Agentic Systems (TAS 2026) treats responsibility as a first-class design objective that must be engineered, measured, and governed across the full lifecycle of an agent, from data and training through deployment, monitoring, and decommissioning.

TAS brings together researchers and practitioners working on subgroup robustness, privacy-preserving and federated learning, safety and alignment, adversarial robustness, explainability, distributed and reliable computation, evaluation, and governance, communities that today publish in separate venues and rarely share a room. The symposium is organized around two complementary themes: trustworthy agentic systems by design, and accountable governance and societal impact. Our objective is to seed a durable, cross-disciplinary AAAI community around responsible and trustworthy agentic AI, and to produce shared resources that outlast the meeting.

Topics

• Privacy-preserving and federated learning, differential privacy, machine unlearning
• Safety, alignment, and guardrails for tool-using agents
• Subgroup robustness and performance disparities across populations
• Adversarial robustness: jailbreaks, prompt and tool-output injection, agent hijacking, red-teaming
• Hallucination mitigation, grounding, factuality, and provenance
• Explainability, interpretability, and transparency for high-stakes decision support
• Specification and verification of agent plans, autonomy levels, and permissions
• Multi-agent coordination and emergent-behavior risks
• Distributed, coded, and reliable computation for agent execution
• Evaluation science: benchmarks, reliability metrics, uncertainty quantification
• Auditing, assurance cases, and incident response for deployed agents
• Governance and standards alignment (NIST AI RMF, ISO/IEC, EU AI Act) as engineering workflows
• Human-AI collaboration, oversight, and accountability
• Empirical studies of adoption, harm, and societal impact

Format

TAS 2026 will be held fully in person. In accordance with AAAI policy, virtual presentations will not be offered, and all presenters and attendees must register for the Fall Symposium Series. The symposium will feature invited speakers, paper presentations, panels, target-problem working groups, breakout discussions, and a tool and dataset showcase, across a fully in-person two and one-half day program. Rather than a sequence of talks, TAS is built around shared target problems and working groups that produce concrete artifacts: open benchmarks, evaluation protocols, red-teaming playbooks, and a community research roadmap. Accepted contributions are circulated to participants in advance so that on-site time is devoted to deeper, more informed exchange. Engaging content is planned across the full program, including the final half day.

Submission Requirements

We invite the following contributions, formatted using the AAAI-26 author kit:

• Full Papers (6-8 pages): for novel research, artifact submissions, or mature works in progress.
• Short Papers (2-4 pages): for position papers, preliminary findings, smaller artifacts, and early-stage work.
• Abstracts (1 page): for sharing ideas (non-archival only).

“Preferred” submission round, with the option to be included in the AAAI proceedings:

• Submission deadline: September 1, 2026
• Author notification: September 15, 2026
• Camera-ready deadline: September 29, 2026

Regardless of archival plans, authors are encouraged to submit earlier rather than later. Page limits exclude references. Each submission is reviewed by the program committee for relevance, soundness, and potential to stimulate discussion. Submissions should be made through EasyChair (https://easychair.org/conferences/?conf=fss26).

Organizing Committee

• Sumon Biswas (Co-Chair, Case Western Reserve University)
• Anindya Bijoy Das (Co-Chair, University of Akron)
• Shahnewaz Karim Sakib (Co-Chair, University of Tennessee at Chattanooga)
• Shibbir Ahmed (Co-Chair, Texas State University)

Website and Contact Information

Website: https://tas2026.github.io/

Contact: sumon@case.edu,  adas@uakron.edu,  shahnewazkarim-sakib@utc.edu, shibbir@txstate.edu

Agentic and Trustworthy AI for Health and the Global AI-Ready Nursing Workforce (AT-AI4H-NW 2026)

This in-person symposium addresses two inseparable halves of one problem in clinical AI: how trustworthy agentic systems are built, certified, and managed safely across their lifecycle under emerging regulatory regimes, and how the nursing and clinical workforce that must adopt these systems is prepared through competency development, education, and governance. The guiding premise is that agentic AI should augment clinical judgment and preserve relational care, with human accountability maintained explicitly throughout design, deployment, and use. The symposium brings AI researchers, clinicians, nurse leaders, informaticists, and regulators together to connect agentic AI development directly to clinical competency and safe adoption.

Topics include (but not limited to): Agentic and generative medical systems (LLM agents in EHR workflows, medical reasoning models, evaluation harnesses, multimodal foundation models); deployment, regulation, and lifecycle management (EU AI Act, FDA AI/ML SaMD, NIST AI RMF, post-deployment monitoring, federated learning); foundations of trust (uncertainty quantification, adversarial robustness, causal AI, human-AI teaming, explainability); nursing competency, clinical integration, and the AI-ready workforce (a competency-to-lifecycle crosswalk grounded in ABNAI’s national nursing AI competency framework, curriculum reform, governance, implementation science). Continuous and home-based sensing, equity, and human accountability cut across all tracks.

Format of Symposium:  invited talks, contributed talks and posters. Panel discussions. 

Submission Requirements

Full papers, 6 to 8 pages; posters, short papers, and position papers, 2 to 4 pages. Work in progress welcome. AAAI format.

We invite the following contributions, formatted using the AAAI-26 author kit:

• Full Papers (6-8 pages): for novel research, artifact submissions, or mature works in progress.
• Short Papers (2-4 pages): for position papers, preliminary findings, smaller artifacts, and early-stage work.
• Abstracts (1 page): for sharing ideas (non-archival only).

“Preferred” submission round, with the option to be included in the AAAI proceedings:

Submission deadline: September 1, 2026

Author notification: September 15, 2026

Camera-ready deadline: September 29, 2026

Submission Site Information: https://easychair.org/conferences/?conf=fss26

Symposium Committee:

Hong Qin, co-chair, Old Dominion University

Apurva Narayan, co-chair, Western University

Catherine Fang, co-chair, Carnegie Mellon University

Vanessa Riley, co-chair, Global Nursing AI Alliance

Simmy King, co-chair, American Board of Nursing Artificial Intelligence

Letu Qingge, North Carolina A&T State University

Ritambhara Singh, Brown University

Laleh Seyyed-Kalantari, York University

Fuhai Li, Washington University in St. Louis

Jennifer Ngure, Global Nursing AI Alliance

Delaney (Wright) La Rosa, Florida State University College of Nursing / ABNAI

Jess Schnyders, Global Nursing AI Alliance

Sharonda Davis, Global Nursing AI Alliance

Lazarus Siafa, Global Nursing AI Alliance

Symposium External URL: https://hongqin.github.io/at_ai4h_nw26/