Intelligenza Artificiale

Scope & Guideline

Transforming Theoretical Concepts into Practical Applications

Introduction

Immerse yourself in the scholarly insights of Intelligenza Artificiale with our comprehensive guidelines detailing its aims and scope. This page is your resource for understanding the journal's thematic priorities. Stay abreast of trending topics currently drawing significant attention and explore declining topics for a full picture of evolving interests. Our selection of highly cited topics and recent high-impact papers is curated within these guidelines to enhance your research impact.
LanguageMulti-Language
ISSN1724-8035
PublisherIOS PRESS
Support Open AccessNo
CountryNetherlands
TypeJournal
Convergefrom 2018 to 2024
AbbreviationINTELL ARTIF / Intell. Artif.
Frequency2 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressNIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS

Aims and Scopes

The journal 'Intelligenza Artificiale' focuses on advancing the field of artificial intelligence through a multidisciplinary approach, integrating theoretical foundations with practical applications. It aims to publish innovative research that enhances the understanding of AI technologies and their implications across various domains.
  1. Artificial Intelligence Methodologies:
    The journal covers a wide range of AI methodologies, including machine learning, neural networks, and symbolic reasoning, emphasizing both theoretical advancements and practical implementations.
  2. Human-Centric AI Applications:
    There is a strong focus on the integration of AI in human-centered applications such as healthcare, education, and social care, highlighting the importance of user experience and ethical considerations.
  3. Interdisciplinary Research:
    The journal encourages interdisciplinary research that combines AI with other fields such as psychology, sociology, and engineering, fostering collaboration and innovation.
  4. Robustness and Safety in AI Systems:
    Research on the robustness, safety, and ethical implications of AI systems is a core area, as the journal seeks to address challenges related to AI deployment in real-world scenarios.
  5. Knowledge Representation and Reasoning:
    The journal emphasizes research on knowledge representation, reasoning, and explainability in AI systems, aiming to improve transparency and interpretability.
Recent publications in 'Intelligenza Artificiale' highlight several trending and emerging themes that reflect the current priorities and innovations in the field of artificial intelligence. These themes indicate a growing interest in practical applications and ethical considerations.
  1. AI in Healthcare and Social Care:
    There is a marked increase in research exploring the application of AI in healthcare and social care settings, focusing on supporting caregivers and addressing multidimensional poverty among older adults.
  2. Explainable AI and Knowledge Extraction:
    Emerging themes in explainable AI and symbolic knowledge extraction are gaining traction, as researchers seek to demystify black-box models and enhance the interpretability of AI systems.
  3. AI for Education:
    The integration of AI in educational settings, particularly in assessing and providing feedback, is a growing area of interest, aiming to improve learning outcomes through intelligent systems.
  4. Human-Agent Interaction:
    Research on empathetic human-agent interaction and symbiotic AI approaches is trending, highlighting the importance of emotional intelligence and user-centered design in AI systems.
  5. Sustainability and AI:
    There is an increasing focus on the role of AI in promoting sustainability, particularly in areas such as environmental monitoring and resource management, reflecting a broader societal emphasis on sustainable practices.

Declining or Waning

While 'Intelligenza Artificiale' continues to evolve, certain themes have shown a decline in prominence over recent publications. This shift may indicate changing research priorities within the AI community or the maturation of previously emerging fields.
  1. Traditional Rule-Based Systems:
    Research on traditional rule-based systems has diminished, as the focus has shifted towards more dynamic and learning-based approaches, such as machine learning and deep learning.
  2. Narrow AI Applications:
    There is a noticeable decline in papers focusing on narrow AI applications, as the field increasingly emphasizes generalizability and adaptability of AI systems across multiple contexts.
  3. Basic Algorithm Development:
    The journal has seen fewer publications on basic algorithm development, suggesting a shift towards applied research that showcases the integration of advanced algorithms in real-world scenarios.
  4. Single-Domain AI Solutions:
    Research focused on AI solutions tailored for single domains has decreased, reflecting a trend towards more interdisciplinary and cross-domain applications.

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