We are excited to be sending out the call for the Neuro-symbolic AI for Agent and Multi-Agent systems workshop at AAMAS-2023. Details below. It is organized by the Turing special interest group (link at the bottom). Perhaps one of these of interest.
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 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 he 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 avery 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 We welcome unpublished technical papers of up to 8 pages, and short (2-4 pages) position papers.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 The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336. Is e buidheann carthannais a th’ ann an Oilthigh Dhùn Èideann, clàraichte an Alba, àireamh clàraidh SC005336. _______________________________________________ uai mailing list uai@engr.orst.edu https://it.engineering.oregonstate.edu/mailman/listinfo/uai
