<html><head></head><body><div class="yahoo-style-wrap" style="font-family:Helvetica Neue, Helvetica, Arial, sans-serif;font-size:13px;"><div dir="ltr" data-setdir="false"><div><p class="ydpec34cdf3MsoNormal" style="margin-bottom:0in">Call for papers</p>
<p class="ydpec34cdf3MsoNormal" style="margin-bottom:0in">Special Issue: <u>Advances in
Intelligent Systems, Technologies and Applications</u></p>
<p class="ydpec34cdf3MsoNormal" style="margin-bottom:0in">Journal: Applied Sciences (ISSN
2076-3417) Impact Factor 2.5</p>
<p class="ydpec34cdf3MsoNormal" style="margin-bottom:0in">Submission<span> </span></p>
<p class="ydpec34cdf3MsoNormal" style="margin-bottom:0in">https://www.mdpi.com/journal/applsci/special_issues/5IQ6DOHB42</p>
<p class="ydpec34cdf3MsoNormal" style="margin-bottom:0in"> </p>
<p class="ydpec34cdf3MsoNormal" style="margin-bottom:0in">Dear Colleague, </p>
<p class="ydpec34cdf3MsoNormal" style="margin-bottom:0in"><b>You are kindly invited to
contribute with a paper to the Special Issue.</b></p>
<p class="ydpec34cdf3MsoNormal" style="margin-bottom:0in">Intelligence in nature is very
diverse; it can be discussed in terms of the intelligence of chimps, dolphins,
and many other animals and even the collective intelligence of very simple life
forms such as ants. The concept of intelligence is being adopted in many
domains of science.</p>
<p class="ydpec34cdf3MsoNormal" style="margin-bottom:0in">From practical and theoretical
perspectives, an essential current research direction that is under examination
by a very large number of scientists worldwide is the development of artificial
systems, also called agent-based systems, which can be either individual agents
or cooperative multiagent systems. These systems are embedded in the
environment, possess a certain degree of autonomy, and are capable of
perceiving the environment and executing actions in it. Intelligence in
agent-based systems can emerge through advanced problem-solving abilities. To
develop these intelligent systems, techniques such as supervised learning and
unsupervised learning play pivotal roles. Supervised learning involves training
systems on labeled datasets, enabling them to map inputs to outputs and improve
their predictions or classifications over time. These methods are extensively
used in applications such as image recognition, natural language processing,
and medical diagnosis. On the other hand, unsupervised learning enables systems
to identify patterns and structures in unlabeled data, facilitating clustering,
anomaly detection, and exploratory data analysis. A more recent paradigm,
retrieval-augmented generation (RAG) and cache-augmented generation (CAG),
combines retrieval- and cache-based methods with generative models to enhance
intelligent systems, producing highly contextualized and accurate responses or
actions. These approaches are instrumental in developing advanced systems for
domains such as conversational AI, knowledge management, and decision support.
Intelligent agent-based systems have many real-world applications, including
the health sciences and industry. The number and diversity of intelligent
systems are increasing rapidly. In this context, the great challenge consists
of creating increasingly intelligent systems. Another critical challenge,
approached by only a few researchers worldwide, is the development of universal
metrics, including black-box-based intelligence metrics, to measure the
intelligence of these systems. Such advances could allow for the comparison of
systems based on their intelligence. In cooperative multiagent systems,
intelligence can be assessed at the system level; this is known as collective
intelligence. Even in very simple cooperative multiagent systems, agents may
interact nonlinearly at various decision points, resulting in emergent
complexity and intelligence at the system level.</p>
<p class="ydpec34cdf3MsoNormal" style="margin-bottom:0in">This Special Issue aims to
establish a solid foundation for future research through a collection of papers
that advance the field by elaborating on theories, designing applications, and
presenting surveys regarding the next generation of increasingly intelligent
systems. The areas of research covered in these papers could include the study
of self-organization, emergence, hybridization, scalability, robustness,
measuring machine intelligence, and the integration of advanced paradigms such
as supervised learning, unsupervised learning, retrieval-augmented generation,
and cache-augmented generation.</p>
<p class="ydpec34cdf3MsoNormal" style="margin-bottom:0in"> </p>
<p class="ydpec34cdf3MsoNormal" style="margin-bottom:0in">Kind regards,</p>
<p class="ydpec34cdf3MsoNormal" style="margin-bottom:0in">Guest Editors</p>
<div style="margin-bottom:0in"><br></div><div style="margin-bottom:0in">Prof. dr. habil. Laszlo Barna Iantovics</div><div style="margin-bottom:0in">Prof. dr. habil. László Kovács</div><div style="margin-bottom:0in">Dr. Attila Biró</div>
<p class="ydpec34cdf3MsoNormal" style="margin-bottom:0in"> </p>
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