BRACIS 2025

The Program Committee of the 35th Brazilian Conference on Intelligent Systems (BRACIS) invites submissions of original research papers for the conference to be held in Fortaleza, CE, Brazil, from September 29th to October 2nd, 2025.

BRACIS is the most important event in Brazil for researchers interested in publishing significant and novel results related to Artificial and Computational Intelligence. The Brazilian Conference on Intelligent Systems (BRACIS) originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence – SBIA (21 editions) and the Brazilian Symposium on Neural Networks – SBRN (12 editions). BRACIS, which previously had 13 editions, will now be recognized as the 35th edition when considering its history and the 21 editions of SBIA. The 35th BRACIS plays a pivotal role in AI in Brazil, serving as a hub for promoting theoretical concepts and applications in Artificial and Computational Intelligence. The event fosters a space for exchanging scientific ideas among researchers, practitioners, scientists, and engineers working toward advancing Artificial and Computational Intelligence science. This aligns with the goals of other major international conferences proposed at a similar time in the history of AI, such as the 37th AAAI, 32nd IJCAI, and 37th NeurIPS (formerly called NIPS). The 34 previous editions of BRACIS highlight the pioneering of the Brazilian AI Community.

IMPORTANT DATES

(all deadlines are 11:59 p.m. UTC-12:00 – anywhere on Earth – AoE!)

– Paper registration – April 28th, 2025 May 7th, 2025

– Paper submission – May 2nd, 2025 May 10th, 2025

– Notification to authors – June 6th, 2025.

– Camera-ready copy due – June 20th, 2025.

SPECIAL ISSUES

Authors of the best papers will be invited to submit extended versions of their work to be appreciated for publication in special issues after the conference.

SUBMISSION DETAILS

BRACIS submissions are double-anonymous. This means that the reviewers’ and authors’ identities and institutions are concealed from the reviewers, and vice versa, throughout the review process. To facilitate this, authors need to ensure that their manuscripts are prepared in a way that does not reveal their identity. Papers that disrespect anonymity will be desk-rejected. We also strongly encourage making code and data available anonymously (e.g., in an anonymous GitHub repository via Anonymous GitHub or in a Dropbox folder).

If you have published a non-anonymous version of your paper online before paper submission (e.g., arXiv), you can send an anonymous version to the conference. No references to the non-anonymous version should be in the anonymous version, and you should let the PC chairs know there is a non-anonymous version. You cannot update the online version nor publish information regarding the work on social media during the paper review period, as it can compromise the double-anonymous review process.

Submitted papers must be written in English and be at most 15 pages, including all tables, figures, references, and appendices. Formatting instructions, as well as templates for Word and LaTeX, are available at Conference Proceedings guidelines. Springer’s proceedings LaTeX templates are also available in Overleaf.

All submitted papers will be reviewed by at least three experts in the field. Accepted papers will be included in the BRACIS proceedings and submitted for publication in Springer in the Lecture Notes in Artificial Intelligence (LNAI) series. Only PDF files can be uploaded to the submission system.

For each accepted paper, at least one author must register for the conference and present the paper at the conference venue.

Submissions must be made online using JEMS3.

ATTENTION 

Generative AI models (including Chat-GPT, BARD, LLaMA, Gemini, etc.) or similar LLMs do not meet the article authorship criteria for BRACIS 2025. However, we encourage articles that describe research on or involving such AI models and tools. Authors who use an LLM in any part of the article writing process take full responsibility for all content, including checking for plagiarism and correcting all text. We suggest that this use be appropriately mentioned in the Acknowledgements section, with no harm in the evaluation process.

***** TRACKS SUBMISSION ****

This year, BRACIS will have four tracks:

1. Main Track: original works showing novel AI methods with sound results.

2. AI Applications for Social Good: original works presenting novel Social Good applications using established AI methods.

3. General Applications: original works presenting novel applications using established AI methods, naturally considering the ethical aspects of the application.

4. Published papers: papers published in top AI conferences or journals in 2023 or 2024 (as a guide, consider the international rankings CS Metrics  and CS rankings by selecting AI area or subareas; others can also be considered). 

Tracks 1-3 will have no distinction regarding the publication format and the publication in the proceedings.  For Track 4, authors must submit a publishable 2-page extended abstract (excluding references) that does not violate the copyright of the previous publication.  Track 4 does not need to be double-blind, as authors must cite the venue of the previous publication. The accepted papers of all tracks will have the same slot for presentation during the conference.

TOPICS OF INTEREST

Submissions should include significant and unpublished research on all aspects of Artificial Intelligence (AI) or Computational Intelligence (CI). Topics of interest include (but are not restricted to):

– Agent-based and Multi-Agent Systems

– AI/CI algorithms and models

– Cognitive Modeling and Human Interaction

– Distributed AI

– Foundations of AI/CI

– Knowledge Representation and Reasoning

– Information Retrieval, Integration, and Extraction

– Model-Based Reasoning

– Automated Reasoning and Approximate Reasoning

– Ontologies and the Semantic Web

– Logic-based Knowledge Representation and Reasoning

– Natural Language Processing

– Data Mining and Analysis

– Machine Learning

– Neural Networks

– Deep Learning

– Reinforcement Learning

– Graph Neural Networks

– Federated Learning

– Planning, Routing and Scheduling

– Evolutionary Computation and Metaheuristics

– Combinatorial Optimization

– Constraint Programming

– Fuzzy Systems

– Meta-learning

– Large Language Models

– Generative AI

– AI and Quantum Computing, Communication, and Technologies

– Pattern Recognition and Cluster Analysis

– Hybrid Systems

– Bioinformatics and Biomedical Engineering using AI

– Computer Vision

– Education for AI and AI for Education

– Game Playing and Intelligent Interactive Entertainment

– Intelligent Robotics and Autonomous Vehicles

– Multidisciplinary AI and CI

– Human-centric AI

– AI Ethics and Societal Impact

– AI for Innovation and Technological Sovereignty

Diego Furtado Silva (ICMC/USP)

Rosiane de Freitas-Rodrigues (IComp/UFAM)