Open access peer-reviewed Edited Volume

Multi-Agent Technologies and Machine Learning

Igor Sheremet

Russian Foundation for Basic Research

An experienced researcher with a notable background in computer science, a corresponding member of the Russian Academy of Sciences, a professor, a deputy director for science of Russian foundation for basic research (RFBR), a deputy chair of RAS Systems analysis committee, and RAS Science board on robotics and mechatronics.


Artificial Intelligence Multi-Agent Technologies Multi-Agent Systems Machine Learning Representation Learning Deep Learning Agent-Based Predictive Modeling Multi-Agent Knowledge Bases Logical Inference Meta-Inference Learning Logic Supervised Learning

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About the book

The book covers one of the most actual and prospective areas of Artificial Intelligence (AI) – Multi-Agent Technologies (MATs) – and their nexus with Machine Learning (ML). MATs and Multi-Agent Systems (MASs) arise as an extremely powerful tool for the solution of various complicated problems regarding the planning of operation of distributed robotics and inter-robot communications, assessment and predictive modeling of situations, smart homes and cities supervising and control, financial, economic, ecological, biological and demographic modeling and simulation, etc. From the other side, ML joins a broad set of theoretical and technological approaches aimed to minimize the difficulty and time necessary for the development and maintenance of knowledge bases of MASs-based socio-technological smart systems. ML is conceptually and practically closely interconnected with artificial neural networks. So joint consideration of these two modern paradigms may produce a non-trivial synergetic effect in both directions: multi-agent implementation of ML logics and procedures, as well as ML-based algorithms of correcting MASs behaviour by changing their knowledge bases according to a volatile environment. The most interesting topic would be combined and deeply integrated MASs with neuro-based ML elements embedded in MAT inference and meta-inference engines.

Publishing process

Book initiated and editor appointed

Date completed: March 1st 2022

Applications to edit the book are assessed and a suitable editor is selected, at which point the process begins.

Chapter proposals submitted and reviewed

Deadline Extended: Open for Submissions

Potential authors submit chapter proposals ready for review by the academic editor and our publishing review team.

Approved chapters written in full and submitted

Deadline for full chapters: May 28th 2022

Once approved by the academic editor and publishing review team, chapters are written and submitted according to pre-agreed parameters

Full chapters peer reviewed

Review results due: August 16th 2022

Full chapter manuscripts are screened for plagiarism and undergo a Main Editor Peer Review. Results are sent to authors within 30 days of submission, with suggestions for rounds of revisions.

Book compiled, published and promoted

Expected publication date: October 15th 2022

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About the editor

Igor Sheremet

Russian Foundation for Basic Research

Dr. Igor Sheremet was born 23/03/1956 in Minsk region, Belarus. He has finished his education at the Academy by Peter The Great, department of electronics and computer science (1977), and at the Russian Presidential Academy, department of state financial policy (2006). He is a corresponding member of the Russian Academy of Sciences (RAS), a professor, a deputy director for science of Russian foundation for basic research (RFBR), a deputy chair of RAS Systems analysis committee and of RAS Science board on robotics and mechatronics. Dr. Sheremet is also a Co-Chair of the Task Group 'Advanced Mathematical Tools for Data-Driven Applied Systems Analysis” of Committee on Data (CODATA) of International Council for Science (ICSU) and an active member of the Science Advisory Committee of International Institute for Applied Systems Analysis (IIASA). During 1977-1993 he has developed a theory and an implementational background of the Augmented Post Systems (APS), providing creation and maintenance of distributed intelligent middleware with very large knowledge and databases. During 1994-2014 Dr. Sheremet has developed and implemented a family of APS-based non-conventional computer architectures, providing deep integration of Multi-Agent Systems, Internet of Things and Big Data paradigms and efficient operation in highly volatile environments. Since 2005 he is successfully developing the theory of recursive multi-sets being a result of the convergence of classical optimization theory and modern knowledge engineering. Main areas of application of this theory are the future digital economy, data-driven applied systems analysis, large socio-technological systems, resource-driven games, as well as the assessment of large economical systems sustainability and vulnerability to various destructive impacts (poor decision making, natural hazards, terror, etc.). Dr. Sheremet has also received several awards, including the L.Euler medal by European Academy of Natural Sciences (2011), Russian Federation Zhukov State Prize (2013), Gold medal and Diploma Di Merito for exceptional achievements by European Scientific-Industrial Chamber (2016), Russian Government Prize (2018).

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Book chapters authored 6

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