Call for Papers | AAMAS 2023: Neuro-symbolic AI for Agent and Multi-Agent systems [NeSyMAS] Workshop
Paper submission link: https://easychair.org/conferences/?conf=nesymas2023
AI has vast potential, some of which has been realised by developments in deep learning methods. However, it has become clear that these approaches have reached an impasse and that such “sub-symbolic” or “neuro-inspired” techniques only work well for certain classes of problem and are generally opaque to both analysis and understanding. “Symbolic” AI techniques, based on rules, logic and reasoning, while not as efficient as “sub-symbolic” approaches, have better behaviour in terms of transparency, explainability, verifiability and, indeed, trustworthiness. A new direction described as “neuro-symbolic” AI combines the efficiency of “sub-symbolic” AI with the transparency of “symbolic” AI. This combination potentially provides a new wave of AI systems that are both interpretable and elaboration tolerant and can integrate reasoning and learning in a very general way.
Though there is work on neuro-symbolic AI for competing with classical ML models, such as its use of label-free supervision and graph embeddings, there is much less on the use for agent modelling or multi-agent systems.
Especially in a multi-agent context, the use of symbolic models for mental state reasoning together with low-level perception patterns or formation of reasoning-capable representations from subsymbolic data, all represent promising areas where MAS offers a unique perspective.
This workshop’s aim is thus to assemble leading-edge work in which neuro-symbolic AI approaches and MAS interact.
TOPICS
Topics of interest include, but are not limited to, the following:
- Explicit agency in neuro-symbolic multi-agent systems
- Neuro-symbolic Reinforcement Learning
- Neuro-symbolic robotics and planning
- Mental models and epistemic logics for MAS
- Multiagency flavours
- Symbolic knowledge representations for subsymbolic MAS
- Neural-symbolic multi-agent systems
- Hybrid agent architectures
- Formal analysis of neural-symbolic multi-agent systems
SUBMISSION
We welcome unpublished technical papers of up to 8 pages, and short (2-4 pages) position papers. Papers should be written in English, be prepared for single-blind reviewing, be submitted as a PDF document, and conform to the formatting guidelines of AAMAS 2023:
https://aamas2023.soton.ac.uk/wp-content/uploads/sites/443/2022/06/AAMAS-2023-Formatting-Instructions.zip
Papers selected for presentation at the workshop will be included in the workshop’s proceedings as open-access publications, tentatively in CEUR (https://ceur-ws.org/) or EPTCS (https://www.eptcs.org/).
Please use the following link to submit your paper:
https://easychair.org/conferences/?conf=nesymas2023
DEADLINES
Important dates [All dates are 23:59 AoE]
- Paper submission deadline: 13 March 2023
- Paper acceptance notification:17 April 2023
- Camera-ready deadline: 15 May 2023
- Workshop: 29 or 30 May, 2023
Organising Committee
Vaishak Belle, University of Edinburgh, UK
Michael Fisher, University of Manchester, UK
Xiaowei Huang, University of Liverpool, UK
Masoumeh Mansouri, University of Birmingham, UK
Albert Meroño-Peñuela, King’s College London, UK
Sriraam Natarajan, UT Dallas, USA
Efi Tsamoura, Samsung Cambridge, UK
This workshop is organised by the Interest Group in Neuro-Symbolic AI of The Alan Turing Institute. You can find more information about us and how to join the Interest Group on our website, https://www.turing.ac.uk/research/interest-groups/neuro-symbolic-ai
Programme Committee [TBC]
Erman Acar (University of Amsterdam)
Artur d’Avila Garcez (City, University of London)
Louise Dennis (University of Manchester)
Devendra Singh Dhami (Hessian Center for AI (hessian.AI) and TU DarmstadtI)
Aaron Eberhart (Kansas State University)
Monireh Ebrahimi (lBM Watson San Francisco)
Andre Freitas (Idiap Research Institute, Switzerland and University of Manchester, UK)
Pascal Hitzler (Kansas State University)
Ian Horrocks (University of Oxford)
Varun Kanade (University of Oxford)
Kristian Kersting (Hessian Center for AI (hessian.AI), DFKI and TU DarmstadtI)
Thomas Kipf (Google Brain)
Zachary Lipton (CMU)
Robert Peharz (TU Graz)
Bei Peng (University of Liverpool)
Francesca Rossi (IBM Research)
Riccardo Tommasini (University of Lyon)
Guy Van den Broeck (UCLA)
Petar Veličković (DeepMind, University of Cambridge)
Christina Winkler (TU München)