[PlanetKR] [CFP] Neuro-symbolic AI for Agent and Multi-Agent systems [NeSyMAS] Workshop

Albert Meroño Peñuela albert.meronyo at gmail.com
Wed Jan 11 13:48:57 UTC 2023


                          CALL FOR PAPERS:
    Neuro-symbolic AI for Agent and Multi-Agent systems [NeSyMAS] Workshop

         [part of AAMAS 2023; London, UK; 29th May-2nd June 2023]

Paper submission link: https://easychair.org/conferences/?conf=nesymas2023
Website: https://albertmeronyo.github.io/nesymas23/

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:

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:

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

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)

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