Logic-based technologies for multi-agent systems: a systematic literature review
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(2021) 35:1
Logic-based technologies for multi-agent systems: a systematic literature review Roberta Calegari1
· Giovanni Ciatto2
· Viviana Mascardi3
· Andrea Omicini2
© The Author(s) 2020
Abstract Precisely when the success of artificial intelligence (AI) sub-symbolic techniques makes them be identified with the whole AI by many non-computer-scientists and non-technical media, symbolic approaches are getting more and more attention as those that could make AI amenable to human understanding. Given the recurring cycles in the AI history, we expect that a revamp of technologies often tagged as “classical AI”—in particular, logic-based ones— will take place in the next few years. On the other hand, agents and multi-agent systems (MAS) have been at the core of the design of intelligent systems since their very beginning, and their long-term connection with logic-based technologies, which characterised their early days, might open new ways to engineer explainable intelligent systems. This is why understanding the current status of logic-based technologies for MAS is nowadays of paramount importance. Accordingly, this paper aims at providing a comprehensive view of those technologies by making them the subject of a systematic literature review (SLR). The resulting technologies are discussed and evaluated from two different perspectives: the MAS and the logic-based ones. Keywords SLR · Logic-based technologies · MAS
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Roberta Calegari [email protected] Giovanni Ciatto [email protected] Viviana Mascardi [email protected] Andrea Omicini [email protected]
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ALMA–AI Interdepartmental Center of Human Centered AI, Alma Mater Studiorum–Università di Bologna, Bologna, Italy
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Dipartimento di Informatica – Scienza e Ingegneria (DISI), Alma Mater Studiorum–Università di Bologna, Bologna, Italy
3
Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi (DIBRIS), Università di Genova, Genoa, Italy 0123456789().: V,-vol
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Autonomous Agents and Multi-Agent Systems
(2021) 35:1
1 Introduction While discussions about the balance between risks and benefits of artificial intelligence (AI) take a significant space in public interest, industry seems to have finally joined (and, maybe, surpassed) academia in valuing AI as one of the pillars of the next industrial revolution. Public institutions have followed, too: for instance, the European Commission already invested 1.5 billion euros in AI for 2018–2020, and many more are planned beyond 2020.1 Furthermore, the “American Artificial Intelligence Initiative: Year One Annual Report”, delivered on February 2020 [334], witnesses the record amounts of AI research and development investment, and the same trend is observed in China.2 When industry, politics, and society focus on new technologies, the promise of a future astonishing progress is no longer enough, since industry leaders, decision makers, and tax payers call for immediate and visible benefits. That is why, following the recent success of deep learnin
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