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.

Matteo Magnini, G.C. (2023). Bridging Symbolic and Sub-Symbolic AI: Towards Cooperative Transfer Learning in Multi-Agent Systems. Aachen : Sun SITE Central Europe, RWTH Aachen University.

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

Matteo Magnini
;
Giovanni Ciatto;Andrea Omicini
2023

Abstract

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.
2023
AIxIA 2022 Discussion Papers
12
22
Matteo Magnini, G.C. (2023). Bridging Symbolic and Sub-Symbolic AI: Towards Cooperative Transfer Learning in Multi-Agent Systems. Aachen : Sun SITE Central Europe, RWTH Aachen University.
Matteo Magnini, Giovanni Ciatto, Andrea Omicini
File in questo prodotto:
File Dimensione Formato  
paper2.pdf

accesso aperto

Descrizione: PDF editoriale
Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione 1.14 MB
Formato Adobe PDF
1.14 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/933657
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact