• Skip to main content
AAAI

AAAI

Association for the Advancement of Artificial Intelligence

    • AAAI

      AAAI

      Association for the Advancement of Artificial Intelligence

  • About AAAIAbout AAAI
    • News
    • Officers and Committees
    • Staff
    • Bylaws
    • Awards
      • Fellows Program
      • Classic Paper Award
      • Dissertation Award
      • Distinguished Service Award
      • Allen Newell Award
      • Outstanding Paper Award
      • AI for Humanity Award
      • Feigenbaum Prize
      • Patrick Henry Winston Outstanding Educator Award
      • Engelmore Award
      • AAAI ISEF Awards
      • Senior Member Status
      • Conference Awards
    • Partnerships
    • Resources
    • Mailing Lists
    • Past Presidential Addresses
    • AAAI 2025 Presidential Panel on the Future of AI Research
    • Presidential Panel on Long-Term AI Futures
    • Past Policy Reports
      • The Role of Intelligent Systems in the National Information Infrastructure (1995)
      • A Report to ARPA on Twenty-First Century Intelligent Systems (1994)
    • Logos
  • aaai-icon_ethics-diversity-line-yellowEthics & Diversity
  • Conference talk bubbleConferences & Symposia
    • AAAI Conference
    • AIES AAAI/ACM
    • AIIDE
    • EAAI
    • HCOMP
    • IAAI
    • ICWSM
    • Spring Symposia
    • Summer Symposia
    • Fall Symposia
    • Code of Conduct for Conferences and Events
  • PublicationsPublications
    • AI Magazine
    • Conference Proceedings
    • AAAI Publication Policies & Guidelines
    • Request to Reproduce Copyrighted Materials
    • Contribute
    • Order Proceedings
  • aaai-icon_ai-magazine-line-yellowAI Magazine
  • MembershipMembership
    • Member Login
    • Chapters

  • Career CenterAI Jobs
  • aaai-icon_ai-topics-line-yellowAITopics
  • aaai-icon_contact-line-yellowContact

  • Twitter
  • Facebook
  • LinkedIn
Home / Conferences / AAAI Conference on Artificial Intelligence / AAAI-25 /

October 28, 2024

The 39th Annual AAAI Conference on Artificial Intelligence

February 25 – March 4, 2025 | Philadelphia, Pennsylvania, USA

  • AAAI-25
  • Attend
    • AAAI-25 Photo Gallery
    • AAAI-25 Know Before You Go
    • Accommodations and Travel
    • Hackathon
    • Job Fair
    • Lunch with a Fellow
    • Onsite Childcare
    • Registration Information
    • Student Activities Overview
    • Student Scholarship and Volunteer Program
    • Walkable Dining
    • Visa Letters of Invitation
  • Program
    • AAAI-25 Paper Awards
    • AAAI-25 Program Overview
    • AAAI-25 Detailed Program Schedule
    • AAAI-25 Invited Talks
    • AAAI-25 Main Technical Track
    • AAAI-25 Poster Sessions
    • Bridge Program
    • Demonstration Program
    • Diversity and Inclusion Activities
    • Doctoral Consortium
    • EAAI Program
    • IAAI-25 Program
    • Journal Track
    • New Faculty Highlights Program
    • Senior Member Presentation Track
    • Student Abstract and Poster Program
    • Tutorial and Lab Forum
    • Undergraduate Consortium
    • Workshop Program
  • Sponsors
    • AAAI-25 Sponsors
    • Become a Sponsor
  • Policies and Guidelines
    • AAAI Code of Conduct for Conferences and Events
    • Policies for AAAI-25 Authors
    • Ethical Guidelines for AAAI-25
    • Poster Information and Printing
    • Publications Ethics and Malpractice Statement
  • Conference Organizers
  • Calls
    • Main Technical Track
    • Special Track on AI Alignment
    • Special Track on AI for Social Impact
    • Bridge Program
    • Demonstration Program
    • Diversity and Inclusion Activities
    • Doctoral Consortium
    • EAAI-25
    • IAAI-25
    • Journal Track
    • New Faculty Highlights
    • Senior Member Presentation Track
    • Student Abstract and Poster Program
    • Tutorial and Lab Forum
    • Undergraduate Consortium
    • Workshops

AAAI-25 Bridge Program

Sponsored by the Association for the Advancement of Artificial Intelligence
February 25-26, 2025 | Pennsylvania Convention Center | Philadelphia, Pennsylvania, USA

B1: AI for Medicine and Healthcare

B2: Bridge between AI and Scientific Knowledge Organization

B3: Bridging Cognitive Science and AI to Bridge Neuro and Symbolic AI

B4: Bridging Planning and Reasoning in Natural Languages with Foundational Models (PLAN-FM)

B5: Collaborative AI and Modeling of Humans 2nd edition

B6: Combining AI and ORMS for better trustworthy Decision Making

B7: Constraint Programming and Machine Learning

B8: Continual Causality

B9: Explainable AI in Energy and Critical Infrastructure Systems

B10: Knowledge-guided Machine Learning: Bridging Scientific Knowledge and AI

B11: Learning for Integrated Task and Motion Planning

B1: AI for Medicine and Healthcare

Our AAAI bridge program aims to address the critical gap between the impressive capabilities of AI in medical research and its limited integration into real-world clinical practice. While AI technologies, such as computer vision algorithms for medical imaging and natural language processing for patient data analysis, have demonstrated great potential, human supervision is often required to ensure safety and reliability.

A key challenge is the disconnect between clinicians, who may lack the technical expertise to fully apply AI tools, and AI developers, who may not completely understand clinical workflows and needs. Our bridge program seeks to bring these two groups together.

Through this initiative, clinicians will gain better insights into how AI can enhance clinical outcomes, while AI developers will learn to design solutions that align with the practical demands of clinical environments. This collaborative effort will foster stronger connections and pave the way for the integration of AI into medical practice.

For further and latest information please check our AIMedHealth website: https://sites.google.com/view/aimedhealth-aaai/home

Format

This event will be one day long. It will include presentation session, training session, workshop session, poster session and a panel talk. We will also invite industry partners to share their perspectives on AI in the medical field. Full schedule can be found: https://sites.google.com/view/aimedhealth-aaai/schedule

Topics

This bridge event aims to integrate techniques from the AI field with insights from clinical practice. By combining expertise from both domains, the bridge will address critical challenges in effectively and safely integrating AI into medical settings. We invite submissions that explore the technical aspects or the clinical perspective of this integration. The following topics are examples, but we encourage a wide range of related contributions to AI for medicine and AI for healthcare. More topic details can be found: https://sites.google.com/view/aimedhealth-aaai/calls.

Submissions

We encourage two formats of submissions:

  • Extended Abstracts: 2-4 pages (exclude reference)
  • Full-Length Papers: Up to 8 pages (exclude reference)

AAAI style template: https://aaai.org/conference/aaai/aaai-25/ 

Link to Submissions: https://easychair.org/conferences/?conf=aimedhealth25

Important Dates

Submission Deadline: November 25, 2024

Notification of Acceptance: December 9, 2024

Early Registration Deadline: December 19, 2024

Bridge Committee
  • Junde Wu, University of Oxford, junde.wu@linacre.ox.ac.uk
  • Jiayuan Zhu, University of Oxford, jiayuan.zhu@keble.ox.ac.uk
  • Min Xu, Carnegie Mellon University, mxu1@cs.cmu.edu
  • Yueming Jin, National University of Singapore, ymjin@nus.edu.sg
  • Alex Novak, Oxford University Hospitals NHSFT, alex.novak@ouh.nhs.uk
  • Sarim Ather, Oxford University Hospitals NHSFT, Sarim.Ather@ouh.nhs.uk
  • Bartek Papiez, University of Oxford, bartlomiej.papiez@bdi.ox.ac.uk
  • Alison Noble, University of Oxford, alison.noble@eng.ox.ac.uk

B2: Bridge between AI and Scientific Knowledge Organization

Description

The scientific community of today faces the problem of scientific papers overload in their respective domains. There is an increasingly large number of currently 3 million papers published every year in addition to the approximatively 200 million ones already published. This gives rise to the research question: “How can we provide a reliable and living scientific knowledge base that empowers researchers to query, synthesize and analyse the vast body of scholarly knowledge?” Although many tools for assisting researchers during scientific knowledge extraction and organization exist, it has been reported that most researchers continue to depend on manual methods. Artificial intelligence (AI) for scholarly communication (AI4SC) aims to leverage AI methodologies, models and applications for scientific knowledge extraction, organisation and use.

The AI4SC bridge program aims to bring together a broad audience (from different disciplines) of students, researchers and AI-experts actively developing/using or not AI-artifacts such as datasets, connectionist and symbolic AI models for scientific knowledge extraction, organization and use to identify common problems, facilitate collaborations and define future research directions. To this end, the following research questions will be considered:

  • How to acquire scientific knowledge from research papers? The aim of this question is to document methodologies, methods, and tools being used for scientific knowledge acquisition. Participants will be invited to describe machine learning (ML) models used during the scientific knowledge acquisition process and datasets used to train these models.
  • How to organise scientific knowledge? Once extracted, scientific knowledge should be organized in such a way that fellow researchers can benefit from it. Participants will describe AI-based models (connectionist and symbolic models) and tools used for scientific knowledge organization.
  • How to improve the usability of tools for knowledge extraction and organization? Tools for knowledge extraction and organization are used in diverse research disciplines including social sciences, engineering and technology, education, environmental sciences, and business and management. Participants will be invited to describe how user interfaces are used for making easy access to scientific knowledge. Thereafter, a panel discussion will be held to discuss how to make these tools more accessible to researchers in all domains.

Given that LLMs is the state-of-the-art in several NLP tasks, a particular attention will be oriented towards the use of LLMs for scientific knowledge extraction and organization.

For more information, visit the following web site: https://sites.google.com/view/ai4sc/edition/ai4sc-AAAI2025 or email us with questions at ai4schocom@gmail.com.

Format

The bridge will include introductory lectures, tutorials, hand on session, panel discussion, demos and poster sessions. The poster session will be devoted to AI-artifacts for scholarly communication.

Submissions

Participants are invited to submit papers of two pages max, excluding references and appendices using Open Review: https://openreview.net/group?id=AAAI.org/2025/Bridge/AI4SC. Given that LLMs is the state-of-the-art in several NLP tasks, a particular attention will be oriented towards the use of LLMs for scientific knowledge extraction, organization or exploitation. The papers should be formatted in the AAAI two column style (see https://aaai.org/authorkit24-2) and be anonymised. Papers that have been submitted/published in other conferences/journals are also welcome.

Participants are invited to submit in the following tracks:

  • Poster track  is dedicated to students and researchers who want to present how they exploit existing literature in their work. A particular attention will be oriented towards the use of AI in scientific communication.
  • Tutorial Track is dedicated to provide participants with practical skills, tools or detailed knowledge in the use of AI in scientific communication. Experts or experienced professionals in the field are invited to submit tutorials including live demonstrations, interactive exercises, or guided practice.
  • Dataset Track aims to showcase new, high-quality datasets and discuss their structure, methodology, and use. The participants are invited to submit papers describing datasets that have potential value for research, analysis, or application of AI for scholarly communication. These datasets can be new or updated ones. Participants should provide the sources of data, describe how the data was collected, cleaned, structured and potential applications.
  • System Track aims to showcase how systems, frameworks, tools or platforms that have been developed for AI in scientific communication work, their design, and their real-world applications. The participants should submit papers describing fully implemented and functional systems.
  • NB: participants can submit to more than one track.
Important Dates

Submissions due: December 10th 2024

Notification to authors: December 20th 2024

Bridge @ AAAI: February, 25-26

Organizers
  • Dr. Azanzi Jiomekong, University of Yaounde
  • Prof. Dr. Sören Auer, Leibniz Universität Hannover

B3: Bridging Cognitive Science and AI to Bridge Neuro and Symbolic AI

Description

This bridge aims to foster a structural and long-lasting connection between cognitive science and AI, focusing on how cognitive mechanisms can inspire neuro-symbolic AI. The objective is to explore how cognitive science, with its insights into human reasoning (both intuitive and deliberate), can inform the integration of data-driven (neuro) and symbolic AI approaches. The ultimate goal is to develop AI systems capable of higher-level reasoning, introspection, and trustworthiness—characteristics that current AI systems lack.

Topics

We invite submissions that address topics related to:

  • Cognitive science contributions to AI architecture and design
  • Integration of neural and symbolic AI
  • Knowledge representation and reasoning in AI
  • Large Language Models (LLMs) and their cognitive inspirations
  • Meta-cognition in AI: how AI systems can self-reflect on their processes and decisions
Format of Bridge

The bridge will be organized into a one-day event, featuring a combination of:

  • Morning Tutorials/Labs: Three interactive sessions to introduce key cognitive science concepts relevant to AI (3 sessions, 1 hour each)
  • Invited Talks: Two talks from leading experts in cognitive science and AI (1 hour each)
  • Paper Presentations: Selected submissions will be presented in two sessions (1 hour each)
  • Panel Discussion: A concluding panel discussion (1.5 hours) featuring key figures from cognitive science and AI to discuss the future of neuro-symbolic AI
Attendance

All presentations must be in person. Only virtual attendance will be permitted and available to everyone registered for the event. There will not be any session recordings shared after the event.

Submission Requirements

We invite submitting original research papers up to 8 pages plus additional pages solely for references. Papers should present innovative solutions, theoretical perspectives, or open challenges relevant to bridging cognitive science and AI.

Papers must be formatted in AAAI two-column, camera-ready style; see the AAAI-25 author kit for details.

Submissions are anonymous and must conform to the instructions (detailed below) for double-blind review.

Bridge Submissions deadline: Sunday, November 24, 2024

Notifications Sent to Authors: Monday, December 9, 2024

Submission Site Information

Submission site

For questions or issues with submission, please email: andrea.loreggia[AT]unibs.it

Bridge Chair

Chair: Francesca Rossi <Francesca.Rossi2@ibm.com>,

Kenneth D Forbus <forbus@northwestern.edu>,

Andrea Loreggia <andrea.loreggia@gmail.com>,

Nicholas Mattei <nsmattei@gmail.com>,

Oscar Romero<oscar.j.romero@gmail.com>,

Brent Venable<brent.venable@gmail.com>,

Biplav Srivastava <BIPLAV.S@sc.edu>

Bridge URL

More detailed information, including the program schedule and speakers, will be available at: https://sites.google.com/view/cosainsai

B4: Bridging Planning and Reasoning in Natural Language with Foundational Models (PLAN-FM)

Description

There is a growing interest in utilizing Foundational Models for complex tasks that require multi-step reasoning and planning. This promising area of research is seeing an increasing number of contributions from researchers in the fields of Natural Language Processing (NLP), Planning, and Robotics. PLAN-FM Bridge program facilitates collaboration and knowledge-sharing among these researchers. This program will provide a stage to discuss and exchange perspectives, identify critical challenges and outline research agendas.

Objective

In this bridge, we aim to foster interactions between NLP, Planning and Robotics researchers that are using and working with foundational models, provide a stage to discuss and exchange common terminology, identify a shared research agenda and pinpoint the most critical challenges, and create a rich repository of resources that can be leveraged by the community, thereby facilitating cross-pollination of ideas and fostering collaboration across research fields to drive the robust advancement of automated planning.

Topics/Open Questions
  • How can we effectively harness the power of foundational models for planning purposes?
  • What are some critical decision-making challenges that can be addressed by leveraging foundational models?
  • What kind of guarantees can we expect when using foundational models for planning?
  • How can we overcome their limited reliability before deploying them in real-world applications?
  • Can we develop a comprehensive suite of datasets and benchmarks that can be shared across communities to evaluate planning abilities in a consistent and reliable manner?
  • What are the key evaluation considerations and metrics that should be used to assess the reliability of planning approaches, and what tools can be leveraged across communities to facilitate this evaluation?
  • What are the characteristics in future foundation models that can help planning?
Format of Bridge

The bridge will provide a rich program of activities, including tutorials, panel discussions, talks, and networking opportunities.  The bridge event will include a half-day tutorial session with a goal of fostering interdisciplinary understanding and exchange of fundamental principles. Second half of the day will include invited talks, panel, and poster session.

We also accept submissions of abstracts, demonstrations as well as position papers.

Submission Requirement

We solicit submissions relevant to the bridge program of the following types:

  • System Demonstration – up to 4 pages (include description of the demo and a screenshot or link)
  • Position papers – up to 4-8 pages (excluding references)
  • Abstracts – up to 2 pages (excluding references)

Papers must be formatted in AAAI two-column, camera-ready style; see the AAAI-25 author kit for details.

Important Dates:
  • Paper submission deadline: Sunday, November 24, 2024 (AOE)
  • Paper acceptance notification: Monday, December 9, 2024 (AOE)

Refer to the bridge website for latest information: https://plan-fm.github.io/

Submission Site Information:

Paper submissions should be made through easychair:  https://easychair.org/conferences/?conf=planfm2025

Bridge Chairs 
  • Harsha Kokel, IBM Research
  • Shirin Sohrabi, IBM Research
  • Soham Dan, Microsoft
  • Manling Li, Northwestern University
  • Yu Su, Ohio State University
Advising Committee:
  • Biplav Srivastava, University of South Carolina
  • Sriraam Natarajan, University of Texas at Dallas
  • Subbarao Khambampati, Arizona State University

Please send your inquiries to plan-fm-bridge@googlegroups.com

Bridge URL:

https://plan-fm.github.io/

B5: Collaborative AI and Modeling of Humans

Advances in Artificial Intelligence (AI) methods have yield unprecedented results, even surpassing human performance on a variety of well-defined tasks. However, most real-world problems, especially those involving humans, are complex, multi-dimensional and hard to specify a priori. A principled way to address this limitation is to allow AI systems to collaborate with humans, and thereby actively anticipate and adapt to humans’ needs and abilities. To enable such reasoning, AI must be equipped with computational models of human behavior. Such models have been heavily investigated in cognitive science and AI-adjacent fields such as human-AI interaction, human-computer interaction (HCI), and behavioral game theory. However, due to differences in research goals and experimental settings, these communities have operated more or less independently, with limited exchange of theories and methods.

Following a successful first edition in 2024, we aim to bring together members of the communities relevant to human-AI collaboration and user modeling to exchange theories, perspectives, and methods.

Topics

The space of disciplines covered by the relevant fields is very large and submissions are expected to cover topics such as:

  • Machine learning with human(s) in the loop
  • User modeling, theory of mind, and computational rationality
  • Human-AI collaboration
Format

This event will be one day long. It will start with two keynote talks, from the perspectives on either side of the bridge topic of human modeling in AI. Next, a tutorial will provide a deep dive into the bridge topic. This will be followed by a poster session where authors of accepted papers will be invited to present their work. The day will conclude with an interactive discussion with a panel of experts with ample time to discuss.

Submissions

We encourage submissions for the poster session on all topics relevant to the bridge but expect they include a dedicated section elucidating the potential interconnection of both disciplines. Submissions are reviewed double-blind, so they should be anonymized. There will be no proceedings, so papers that have been or will be submitted or published in other conferences or journals are also welcome.

We accept papers of 2 to 8 pages, excluding references and appendices. The papers should be formatted in the AAAI two-column, camera-ready style (see https://aaai.org/authorkit25 for details) and authors can submit their works through  https://openreview.net/group?id=AAAI.org/2025/Bridge/CAIHU.

For more detailed submission instructions, please visit https://sites.google.com/view/caihu25/call-for-papers?authuser=0.

Target Audience

In this bridge program, we hope to bring together a broad audience of students, researchers, and practitioners in fields relevant to human-AI collaboration and human behavior modeling including AI, HCI, and CogSci.

Organizers
  • Andrew Howes; University of Exeter, England; A.Howes2@exeter.ac.uk
  • Samuel Kaski; Aalto University, Finland and University of Manchester, UK; samuel.kaski@aalto.fi
  • Frans A. Oliehoek; Delft University of Technology, Netherlands; f.a.oliehoek@tudelft
  • Nuria Oliver; ELLIS Alicante, Spain; nuria@ellisalicante.org
  • Matthew E. Taylor; University of Alberta & Alberta Machine Intelligence Institute, Canada; matthew.e.taylor@ualberta.ca
Additional information

For more information visit https://sites.google.com/view/caihu25/home?authuser=0 or email us with questions at caihu.aaai@gmail.com.

B6: Combining AI and ORMS for Better Trustworthy Decision Making

Description

Artificial Intelligence (AI), including Generative AI, and Operations Research/Management Science (OR/MS) offer proven but distinct approaches to decision-making with data and models. However, challenges persist in applying them to vital socio-technical environments where human and artificial systems interact. These include the need to combine AI and OR/MS for the best solution, aligning models with human values and promoting trust, and the expertise and time required for their application, which limit wider use.

Main Objectives

The goal of this bridge program is to unite AI and OR/MS practitioners and researchers to improve trustworthy decision-making in key socio-technical areas such as supply chains, healthcare, crisis management, homeland security, robotics, wildlife conservation, medicine, transportation, and finance. It aims to equip them with better tools by familiarizing them with each other’s techniques and domains and bring the disciplines together to advance the research and applications at the intersection of AI and OR/MS so as to improve decision-making.

Topics
  • Utilizing AI, OR/MS, and their integration for decision-making.
  • Exploring current state-of-the-art research and identifying new directions in combining AI and OR/MS for improved trustworthy decision-making, including integrating Large Language Models with OR/MS and other AI tools to democratize advanced decision-making capabilities and integrating OR/MS and AI to improve trustworthiness.
  • Identifying key domains and use cases where AI and OR/MS can improve decision-making.
Format of the Bridge

This two-day bridge will include: a half day of invited talks and a tutorial on using OR/MS for decision-making; a half day of submitted presentations and posters on applying AI and/or OR/MS and state-of-the-art research on their integration for trustworthy decision-making; a half day of presentations on future research directions and key domains for AI and OR/MS integration; and a half day of discussions to refine research priorities and focus use cases, and define next steps.

Attendance

Attendance is open to all interested students, practitioners, and researchers.

Submission Requirements

Participants interested in presenting can submit a presentation proposal about one or more of the following:

  • An application of AI and/or OR/MS (individually or together) to decision-making.
  • Existing research integrating AI and OR/MS for decision-making.
  • New, important research directions that integrate AI and OR/MS for decision-making.
  • Decision-making domains and use cases where AI and OR/MS should be jointly applied, with a rationale for the combined approach.
  • Relevant surveys presentation.

Submissions can range from a one-page abstract to a full journal article and may include work at any level of maturity, as well as previously published work.

Submission Site Information: https://easychair.org/conferences/?conf=aiorms2025

Bridge Committee:
  • Sven Koenig, University of California, Irvine, sven.koenig@uci.edu
  • Michela Milano, Università di Bologna, michela.milano@unibo.it.
  • Willem-Jan van Hoeve, Carnegie Mellon University, vanhoeve@andrew.cmu.edu.
  • Segev Wasserkrug, IBM Research and Technion, segevw@il.ibm.com.

Bridge External URL: https://aaai.org/conference/aaai/aaai-25/bridge-ai-orms/

B7: Constraint Programming and Machine Learning

Bringing together Constraint Programming (CP) and Machine Learning (ML) is an important aspect of the larger goal of integrating Reasoning and Learning. Participants are not expected to have prior experience in both fields, but to have familiarity with each at least at the level of an introductory AI course. The Bridge is designed to educate and to build community, to provide opportunities to interact, discuss, raise awareness and find collaborators.

Focus

The focus of this one-day Bridge will be on bringing together the traditional AI fields of constraint-based reasoning and machine learning, but participants from related fields of reasoning, optimization and learning, e.g. SAT, operations research, data mining, will be welcome.

Submissions

You can submit in any of a variety of Tracks. There are many Tracks. We do not necessarily expect to receive submissions for every Track, but we wish to maximize opportunities and options for contributing to the Bridge and the Bridge community. You may submit to more than one Track. The simplest option is the Introductions Track, which has minimal requirements, and provides an opportunity for participants to introduce themselves, with a view to facilitating interaction and enabling collaboration, during the Bridge day and afterwards. Note that if we receive too many submissions to this Track to accommodate for physical presentation in the time available, appropriate participants will be chosen for presentation on a first come first served basis, so you are encouraged to submit early.

The full list of Track options is available at the CPML Bridge website, along with full submission requirements and instructions and other important information. Submissions will be through EasyChair.

Important Dates
  • November 25, 2024: Bridge Submissions Due
  • December 9, 2024: Notifications Sent to Authors
  • December 19, 2024: AAAI Early Registration Deadline
Organizers

Eugene Freuder. UCC. eugene.freuder@insight-centre.org

Barry O’Sullivan. UCC. barry.osullivan@insight-centre.org

Steering Committee

Christian Bessiere (U.Montpellier, LIRMM)

Luc De Raedt (KU Leuven)

Eugene Freuder (University College Cork)

Tias Guns (KU Leuven)

Kevin Leyton-Brown (Uni. of British Columbia)

Michela Milano (University of Bologna)

Nina Narodytska (VMware Research, USA)

Barry O’Sullivan (University College Cork)

B8: Continual Causality

The fields of causality and continual learning investigate complementary aspects of human cognition, and artificial intelligence must emulate both if it is to reason and generalize in complex environments. On the one hand, causality theory provides the language, algorithms, and tools to discover and infer cause-and-effect relationships from data. On the other hand, continual learning systems balance learning from new data as they become available with retaining previous knowledge while experiencing distribution shift over time. Our recurring “Continual Causality” bridge continues working towards a unified treatment of these fields by providing a space to learn and discuss, and to connect and build a diverse long-term community.

Topics

We invite submissions that present general positions or visions of how to link the two fields, outline challenges that need to be overcome, highlight synergies, or discuss first practical approaches and solutions to relevant problems. Our vision is for the community to voice diverse views that have the potential to advance AI through an ongoing cross-disciplinary exchange.

Format of Bridge

Our two day bridge is composed of a wide range of activities, including traditional tutorials on the educational side, invited vision talks and contributed ones based on submitted papers, interactive sessions in the form of a speakers panel, community breakout discussions, and a challenge session.

Attendance

We invite contributions in the form of original papers, recently published works on the bridge’s theme, and challenge submissions. All attendees of AAAI-25 are further invited to actively participate in our discussion and breakout sessions.

Submission Requirements

Submission can either be non-archival or for inclusion in a Proceedings of Machine Learning Research (PMLR) volume. However, all works must be original and limited to four pages (excluding references and optional appendices) in the AAAI format. The deadline for submissions is November 25, 2024 (AOE). The review process is double-blind, so submissions should be anonymized.

Submission Site Information: submissions will be managed through OpenReview: https://openreview.net/group?id=AAAI.org/2025/Bridge/Continual_Causality. New this year: we also host a challenge and allow for presentations of recently published works. These follow a separate call for participation at https://www.continualcausality.org

Bridge Committee

Keiland Cooper – University of California – kwcooper@uci.edu

Rebecca Herman – TU Dresden – rebecca.herman@tu-dresden.de

Martin Mundt –  University of Bremen & TU Darmstadt – mundtm@uni-bremen.de

P. K. Srijith – IIT Hyderabad – srijith@cse.iith.ac.in

Devendra Singh Dhami – TU Eindhoven – d.s.dhami@tue.nl

Roshni Kamath – TU Darmstadt & hessian.AI – roshni.kamath@tu-darmstadt.de

Florian Busch – TU Darmstadt & hessian.AI – florian_peter.busch@tu-darmstadt.de

Bridge External URL

https://www.continualcausality.org

B9: Explainable AI, Energy and Critical Infrastructure Systems

The goal of this bridge will be to bring together researchers, practitioners from industry and policymakers to share technical advances and insights on applications and challenges for the application of AI in energy and critical infrastructure systems. The target audience includes AI researchers that are actively working on the use of AI and XAI in energy and critical infrastructure systems, as well as researchers in energy and critical infrastructure systems that would like to explore the potential benefits of applying AI and XAI to these domains. Other stakeholders, including policymakers and regulators interested in XAI, energy and critical infrastructure systems are welcome to join.

Format of Bridge

This 1-day bridge will include invited talks, panels and tutorials covering a wide range of topics at the intersection of XAI, Energy and Critical Infrastructure Systems. This bridge will also include networking sessions and mentoring opportunities for students.

Attendance

No fixed criteria to participate.

Submission Requirements

This Bridge accepts submissions to a variety of Tracks, which are described in the Bridge website. Our aim is to maximize opportunities and options for contributing to this Bridge and the Bridge community. Submissions to multiple Tracks are allowed.

Submissions need to discuss interactions between XAI, Energy and Critical Infrastructure systems in some fashion. All submissions should keep in mind that Bridge attendees are not necessarily experts in both fields. Presentations should try to bridge potential gaps, and welcome questions and discussion.

Submissions should be prepared using the AAAI 2025 template available here. Submissions should specify the Track they are submitted to, contain the names, affiliations and contact emails of the authors, and indicate which authors would be expected to attend the Bridge.

The full list of Track options is available at the bridge website along with full submission requirements and instructions and other important information. Submissions will be through EasyChair. The deadline for submitting to this bridge is November 25th, 2024. All accepted contributions must be presented in person. All participants, whether they have an accepted contribution or not, will be required to register to AAAI 2025 using the Bridge, Tutorial and Lab only registration.

All accepted submissions will be posted on this website. If a submission is accepted for physical presentation, authors will then be expected to provide any slides used in the presentation for inclusion in the Bridge website as well.

Submission Site Information: https://easychair.org/my/conference?conf=xaieci2025

Bridge Chair:

Francesco Leofante, Imperial College London, f.leofante@imperial.ac.uk

Bridge Committee:

Francesco Leofante, Imperial College London, f.leofante@imperial.ac.uk

André Artelt, Bielefeld University, aartelt@techfak.uni-bielefeld.de

Demetrios Eliades, University of Cyprus, eliades.demetrios@ucy.ac.cy

Anna Korre, Imperial College London, a.korre@imperial.ac.uk

Tim Miller, The University of Queensland, timothy.miller@uq.edu.au

Francesca Toni, Imperial College London, f.toni@imperial.ac.uk

Bridge External URL: https://www.doc.ic.ac.uk/~fleofant/aaai25-xai-eci/index.html

B10: Knowledge-guided Machine Learning: Bridging Scientific Knowledge and AI

Description

Knowledge-guided machine learning (KGML) is an emerging field of research that focuses on integrating scientific knowledge in ML frameworks to produce solutions that are scientifically grounded, explainable, and likely to generalize on out-of-distribution samples, even with limited training data. By using both scientific knowledge and data as complementary sources of introduction in the design, training, and evaluation of ML models, KGML seeks a distinct departure from black-box data-only methods and holds great potential for accelerating scientific discovery in a number of disciplines. The goal of our bridge 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 KGML.

Topics

We encourage participation on a range of topics exploring the synergy between scientific knowledge and ML, including (but not limited to): (a) use of scientific knowledge as loss functions or hard constraints in the training of ML models for supervised, unsupervised, and semi-supervised applications, (b) design of deep learning architectures that are grounded in scientific theories and generate explainable and physically meaningful feature representations, (c) use of simulated data generated by science-based models along with observations in ML frameworks, (d) techniques to augment imperfections or infer parameters in science-based models using ML, and (e) use of scientific knowledge in the design, pretraining, or finetuning of Foundation models in science.

Format

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

Submission Requirements

We are accepting short submissions (maximum 2 pages excluding references) as extended abstracts or proposals in a variety of tracks such as: (a) Blue Sky Ideas papers providing a position or perspective of a research area in KGML, (b) Tutorials (lecture-style or hands-on demos) of KGML topics, (c) Posters showing preliminary results on cutting-edge research problems, (d) Datasets and Benchmarks papers relevant to KGML, (e) early career lightning talks promoting next-generation leaders in KGML including postdocs and early career investigators, and (f) dissertation forum submissions for graduate students to present their dissertation research in KGML. All submissions will undergo light review by the organizers for suitability for the bridge.

Submission Site Information: https://easychair.org/my/conference?conf=kgmlbridgeaaai25

Organizing Committee
  • Arka Daw (dawa@ornl.gov)
  • Nikhil Muralidhar (nmurali1@stevens.edu)
  • Taniya Kapoor (t.kapoor@tudelft.nl)
  • Kai-Hendrik Cohrs (kai.cohrs@uv.es) 
Steering Committee
  • Anuj Karpatne (karpatne@vt.edu)
  • Xiaowei Jia (xiaowei@pitt.edu)
  • Ramakrishnan Kannan (kannanr@ornl.gov)
  • Vipin Kumar (kumar001@umn.edu)

Bridge URL: https://sites.google.com/vt.edu/kgml-bridge-aaai-25/

B11: Learning for Integrated Task and Motion Planning

Description:

Robotic agents are required to accomplish increasingly complex and longer-horizon tasks autonomously. This requires developing novel approaches for computing increasingly elaborate and robust plans that optimize the agents’ behavior and allow them to deal with unexpected events.

Effective solution approaches for such settings need to manage a rich coupling between three levels of abstraction – task, motion, and control. However, effectively integrating these three components has been established as a challenging sequential decision-making problem that requires integrating skills and tools from different research disciplines, investigated by different research communities which makes the integration of motions and high-level actions especially challenging.

Our proposed bridge program aims to bring together researchers from different research communities and help catalyze the next generation of research in combining AI, machine learning, and robotics and developing robots that are capable and efficient at all levels of deliberation and decision-making

Topics

Our bridge program will offer challenge problems, tutorials, laser talks, and panels on major elements of TMP and learning for TMP that are required to develop capable and dexterous autonomous robotic systems. The content will center around various related themes including motion planning, task planning, robust execution and control, perception, and manipulation, planning under uncertainty and risk, imitation and reinforcement learning, and more.

Format

We propose a two-day program. Each day will start with two lectures given by prominent researchers, including speakers who perform research at the crossover between learning, robotics, and AI. It will also include vision and challenge talks, to set the context for the field.
Because lectures alone are not enough to have a lasting impact, we will complement them with two hands-on lab sessions each day. During the labs, participants will implement ideas discussed in the talks and will solve problems in a simulated robotic setting.

To encourage the exchange of ideas, at the end of each lab session, we will facilitate a discussion on challenges that were encountered during the labs and potential solutions approaches. To further foster discussions, participants will be asked to provide 1-2 minute laser talks and will be encouraged to bring posters, which will be available throughout the day, with dedicated poster sessions during the breaks.

Attendance

The intended audience includes graduate students, postdocs, and researchers interested in developing capable real-world robotic systems, enabled through task and motion planning systems that combine a balance of model-based and machine learning methods. Of particular relevance are researchers from machine learning, perception, AI, Robotics, and control who are interested in this enterprise.

Bridge Committee

Sarah Keren, Technion – Israel Institute of Technology – sarahk@cs.technion.ac.il

Brian Williams, Massachusetts Institute of Technology – williams@csail.mit.edu

Michael Posa, University of Pennsylvania – posa@seas.upenn.edu

Bridge URL: https://github.com/CLAIR-LAB-TECHNION/AAAI_25_Bridge_TMP


Categories: AAAI Conference

We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking “Accept All”, you consent to the use of ALL the cookies. However, you may visit "Cookie Settings" to provide a controlled consent.
Cookie SettingsAccept All
Manage consent

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary
Always Enabled
Necessary cookies are absolutely essential for the website to function properly. These cookies ensure basic functionalities and security features of the website, anonymously.
CookieDurationDescription
cookielawinfo-checkbox-analytics11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
cookielawinfo-checkbox-functional11 monthsThe cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
cookielawinfo-checkbox-necessary11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-others11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
cookielawinfo-checkbox-performance11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
viewed_cookie_policy11 monthsThe cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.
Functional
Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features.
Performance
Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.
Analytics
Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc.
Advertisement
Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. These cookies track visitors across websites and collect information to provide customized ads.
Others
Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet.
SAVE & ACCEPT