Bridging Symbolic and Sub-Symbolic AI: Towards Cooperative Transfer Learning in Multi-Agent Systems

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Agostino Dovier, Angelo Montanari, Andrea Orlandini (eds.)
AIxIA 2022 Discussion Papers, chapter 2, pages 12–22
CEUR Workshop Proceedings (AIxIA Series) 3419
Sun SITE Central Europe, RWTH Aachen University, Aachen, Germany
June 2023

Cooperation and knowledge sharing are of paramount importance in the evolution of an intelligent species. Knowledge sharing requires a set of symbols with a shared interpretation, enabling effective communication supporting cooperation. The engineering of intelligent systems may then benefit from the distribution of knowledge among multiple components capable of cooperation and symbolic knowledge sharing. Accordingly, in this paper, we propose a roadmap for the exploitation of knowledge representation and sharing to foster higher degrees of artificial intelligence. We do so by envisioning intelligent systems as composed by multiple agents, capable of cooperative (transfer) learning—Co(T)L for short. In CoL, agents can improve their local (sub-symbolic) knowledge by exchanging (symbolic) information among each others. In CoTL, agents can also learn new tasks autonomously by sharing information about similar tasks. Along this line, we motivate the introduction of Co(T)L and discuss benefits and feasibility.

keywordstransfer learning; multi-agent systems; artificial general intelligence; symbolic knowledge extraction; symbolic knowledge injection
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page_white_powerpointBridging Symbolic and Sub-Symbolic AI: Towards Cooperative Transfer Learning in Multi-Agent Systems (AIxIA 2022, 29/11/2022) — Matteo Magnini (Matteo Magnini, Giovanni Ciatto, Andrea Omicini)
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page_white_powerpointBridging Symbolic and Sub-Symbolic AI: Towards Cooperative Transfer Learning in Multi-Agent Systems (AIxIA 2022, 29/11/2022) — Matteo Magnini (Matteo Magnini, Giovanni Ciatto, Andrea Omicini)