Connectionists: Advances in Intelligent Systems, Technologies and Applications

[Barna Iantovics]

Call for papers

Special Issue: Advances inIntelligent Systems, Technologies and Applications

Journal: Applied Sciences (ISSN2076-3417) Impact Factor 2.5

Submission  

https://www.mdpi.com/journal/applsci/special_issues/5IQ6DOHB42

 

Dear Colleague, 

You are kindly invited tocontribute with a paper to the Special Issue.

Intelligence in nature is verydiverse; it can be discussed in terms of the intelligence of chimps, dolphins,and many other animals and even the collective intelligence of very simple lifeforms such as ants. The concept of intelligence is being adopted in manydomains of science.

>From practical and theoreticalperspectives, an essential current research direction that is under examinationby a very large number of scientists worldwide is the development of artificialsystems, also called agent-based systems, which can be either individual agentsor cooperative multiagent systems. These systems are embedded in theenvironment, possess a certain degree of autonomy, and are capable ofperceiving the environment and executing actions in it. Intelligence inagent-based systems can emerge through advanced problem-solving abilities. Todevelop these intelligent systems, techniques such as supervised learning andunsupervised learning play pivotal roles. Supervised learning involves trainingsystems on labeled datasets, enabling them to map inputs to outputs and improvetheir predictions or classifications over time. These methods are extensivelyused in applications such as image recognition, natural language processing,and medical diagnosis. On the other hand, unsupervised learning enables systemsto 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 enhanceintelligent systems, producing highly contextualized and accurate responses oractions. These approaches are instrumental in developing advanced systems fordomains such as conversational AI, knowledge management, and decision support.Intelligent agent-based systems have many real-world applications, includingthe health sciences and industry. The number and diversity of intelligentsystems are increasing rapidly. In this context, the great challenge consistsof creating increasingly intelligent systems. Another critical challenge,approached by only a few researchers worldwide, is the development of universalmetrics, including black-box-based intelligence metrics, to measure theintelligence of these systems. Such advances could allow for the comparison ofsystems based on their intelligence. In cooperative multiagent systems,intelligence can be assessed at the system level; this is known as collectiveintelligence. Even in very simple cooperative multiagent systems, agents mayinteract nonlinearly at various decision points, resulting in emergentcomplexity and intelligence at the system level.

This Special Issue aims toestablish a solid foundation for future research through a collection of papersthat advance the field by elaborating on theories, designing applications, andpresenting surveys regarding the next generation of increasingly intelligentsystems. The areas of research covered in these papers could include the studyof self-organization, emergence, hybridization, scalability, robustness,measuring machine intelligence, and the integration of advanced paradigms suchas supervised learning, unsupervised learning, retrieval-augmented generation,and cache-augmented generation.

 

Kind regards,

Guest Editors

Prof. dr. habil. Laszlo Barna IantovicsProf. dr. habil. László KovácsDr. Attila Biró
 

 

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