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Proceedings of the AAAI Conference on Artificial Intelligence (PAAAICAI)

Publisher :

Association for the Advancement of Artificial Intelligence

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Peer reviewed only
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Open Access
  • Artificial Intelligence
e-ISSN :

2374-3468

Issue Frequency :

Monthly

p-ISSN :

2159-5399

Est. Year :

1980

Mobile :

12023604062

Country :

United States

Language :

English

APC :

YES

Impact Factor Assignee :

Google Scholar

Email :

publications@aaai.org, proceedings-questions@aaai.org

Journal Descriptions

The Thirty-Ninth AAAI Conference on Artificial Intelligence was held on February 25 – March 4, 2025, Philadelphia, Pennyslvania. The program chairs were Julie Shah (Massachusetts Institute of Technology, USA) and Zico Kolter (Carnegie Mellon University, USA). AAAI-25 welcomed submissions on research that advances artificial intelligence, broadly conceived. The conference featured technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs. Many of these activities were tailored to the theme of bridges and were selected according to the highest standards, with additional programs for students and young researchers. In addition to the Main Technical Track, authors were encouraged to submit papers for the Special Track on AI for Social Impact and the Special Track on AI Alignment. Driven by its disciplinary diversity, AAAI has incubated numerous AI sub-disciplines and conferences and has nurtured for decades the cohesion of AI. The purpose of this year’s Bridge Program is to tap into new sources of innovation by cultivating collaboration between two or more communities directed towards a common goal. Hence, the communities that our Bridge Program is intended to bring together could be distinct subfields of AI, such as planning and learning, or different disciplines that contribute to and benefit from AI, such as AI and the humanities. The conference scope included machine learning, natural language processing, computer vision, data mining, multiagent systems, knowledge representation, human-in-the-loop AI, search, planning, reasoning, robotics and perception, and ethics. In addition to fundamental work that focused on any one of these areas, AAAI-25 encouraged work across technical areas of AI, (e.g., machine learning and computer vision; computer vision and natural language processing; or machine learning and planning), bridges between AI and a related research area (e.g., neuroscience; cognitive science) or developing AI techniques in the context of important application domains, such as healthcare, sustainability, transportation, and commerce. The conference also continued its tradition of collocating with the long-running Innovative Applications of Artificial Intelligence conference (IAAI-25). IAAI-25 was cochaired by Jan Seyler (Festo, Germany), Serdar Kadioglu (Brown University, USA) and Sean McGregor (UL Research Institutes, USA). The IAAI-25 papers are included in this proceedings. Also included are the papers from the Symposium on Educational Advances in Artificial Intelligence (EAAI-25). EAAI-25 was cochaired by Stephanie Rosenthal (Carnegie Mellon University, USA) and Narges Norouzi (University of California Berkley, USA)


Proceedings of the AAAI Conference on Artificial Intelligence (PAAAICAI) is :

International, Peer-Reviewed, Open Access, Refereed, Artificial Intelligence , Online or Print, Monthly Journal

UGC Approved, ISSN Approved: P-ISSN - 2159-5399, E-ISSN - 2374-3468, Established in - 1980, Impact Factor

Not Provide Crossref DOI

Indexed in PubMed

Not indexed in Scopus, WoS, DOAJ, UGC CARE

Indexing

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Publications of PAAAICAI

  • dott image June, 2022

Algorithmic Concept-Based Explainable Reasoning

Recent research on graph neural network (GNN) models successfully applied GNNs to classical graph algorithms and combinatorial optimisation problems. This has numerous benefits, such as allo...

  • dott image June, 2022

Entropy-Based Logic Explanations of Neural Networks

Explainable artificial intelligence has rapidly emerged since lawmakers have started requiring interpretable models for safety-critical domains. Concept-based neural networks have arisen as ...

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