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Call for Papers

General Information:

Submissions are invited for the 4th International Joint Conference on Learning and Reasoning, which is planned to be held in Nanjing, China, from September 20th to 22nd, 2024. Since 2021, IJCLR has aimed at being the conference bringing together the international AI community that is interested in the research of integrating learning and reasoning for addressing many of the shortcomings of contemporary AI approaches, including the black-box nature and the brittleness of deep learning, and the difficulty to adapt knowledge representation models in the light of new data.

The authors could submit their papers to the Journal Track, Main Track, Extended Abstract Track, Recently Published Papers Track or the Special Track (Formal Reasoning and Large Language Models); these tracks will post their calls for papers later this year, and their deadlines, procedures, policies may differ from what is described below.

Submissions are solicited on all aspects of Learning and Reasoning and topics where machine learning is combined with machine reasoning or knowledge representation.

Authors are invited to submit novel, high-quality work that has neither appeared in nor is under consideration for publication by other journals or conferences (except for the Recently Published Papers Track).

Topics of interest for the Journal Track include, but are not limited to:

  • Theory & foundations of logical & relational learning.
  • Learning in various logical representations and formalisms, such as logic programming & answer set programming, first-order & higher-order logic, description logic & ontologies.
  • Inductive methods for program synthesis or example-driven programming.
  • Combining logic and functional program induction, meta-interpretative learning & predicate invention.
  • Statistical Relational AI, including structure/parameter learning for probabilistic logic languages, relational probabilistic graphical models, kernel-based methods, neural-symbolic learning.
  • Systems and techniques that integrate neural, statistical & symbolic learning.
  • Systems and techniques addressing aspects of integrating learning, reasoning & optimization.
  • Knowledge representation and reasoning in deep neural networks.
  • Symbolic knowledge extraction from neural and statistical learning models.
  • Neural-symbolic cognitive models.
  • Techniques that foster explainability & trustworthiness of AI models, including combinations of machine learning with constraints & satisfiability, explainable AI frameworks and reasoning about the behavior of machine learning models.
  • Scaling-up logical & relational learning: parallel & distributed learning techniques, online learning and learning structured representations from data streams.
  • Human-Like Computing, including Cognitive and AI aspects of perception, action and learning.



Main Track:

Conference papers, describing original work with appropriate experimental evaluation and/or a self-contained theoretical contribution. Submitted conference papers should not have been published, or be under review for a journal, or another conference with published proceedings. Conference papers may be either long papers, of up to 15 pages, including references, or short papers of up to 6-9 pages, including references. Submitted papers should contain a substantial contribution that justifies their length, e.g. proofs of extensive experimental studies.

Visit the Important Dates page for the Main Track cut-off dates. Submission to the IJCLR Main Track will be made online in May.

Visit the Submission Guidelines page for more information.

Papers accepted to the Main Track will be presented at IJCLR.

Journal Track:

IJCLR’s Journal Track, the Special Issue on Learning and Reasoning is supported by the Machine Learning Journal (MLJ) and will accept paper submissions on regular cut-off dates.

Visit the Important Dates page for the Journal Track cut-off dates.

Visit the Submission Guidelines page for more information.

Journal Track papers are published online by the MLJ upon acceptance and authors of accepted papers are invited to present their work at IJCLR.

Recently Published Papers Track:

IJCLR invites high-quality papers relevant to the scope of the conference, which have been recently published, or accepted for publication, by a first-class conference such as IJCAI, AAAI, NeurIPS, ICML, KDD, ECML/PKDD, ICDM, etc., or journals such as AIJ, MLJ, DMKD, JMLR, etc.

Visit the Important Dates page for the Recently Published Papers Track cut-off dates.

Visit the Submission Guidelines page for more information.

Papers submitted to the “Recently Published Papers Track” will be accepted on the grounds of relevance and quality of the original publication venue.

Accepted papers will be presented at IJCLR.

Special Track:

IJCLR’24 also invites papers submitted to the Special Track on “Formal Reasoning and Large Language Models”. This unique track aims to bridge the gap between the rigorous theoretical underpinnings of formal reasoning and the cutting-edge advancements in large language models (LLMs) or other types of foundation models. The Special Track aims to encourage pushing the boundaries of what’s possible when combining the soundness of formal reasoning with the versatility of large language models.

The Special Track invites submissions of original research that explores innovative intersections, methodologies, applications, and theoretical insights into the integration of formal reasoning approaches with LLMs. Topics of interest include but are not limited to the application of formal methods in training and interpreting LLMs, leveraging LLMs for enhancing formal reasoning systems, novel approaches to integrating logical reasoning within LLM frameworks, and empirical studies that evaluate the effectiveness of LLMs in formal reasoning tasks.

The page length limit and important dates of the Special Track will be the same as the Main Track. Visit the Submission Guidelines page for more information.

Publication:

Conference papers (either short or long) will be included in the conference proceedings, to be published by Springer Verlag in the Lecture Notes in Artificial Intelligence (LNAI) Series.

Late Breaking abstracts will be published on the conference website. A link to recently published papers will be uploaded to the conference website.