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EGNN: A Deep Reinforcement Learning Architecture for Enforcement Heuristics

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FAIA_353_FAIA220169.pdf (106.87 KB)

Publication date

2022-09-07

Authors

Craandijk, DennisISNI 0000000492830166
Bex, FlorisORCID 0000-0002-5699-9656ISNI 0000000118066508

Editors

Toni, Francesca
Polberg, Sylwia
Booth, Richard
Caminada, Martin
Kido, Hiroyuki

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Supervisors

DOI

https://doi.org/10.3233/FAIA220169

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Part of book

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Utrecht University Repository
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cc_by_nc

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Citation

Craandijk, D & Bex, F 2022, EGNN: A Deep Reinforcement Learning Architecture for Enforcement Heuristics. in F Toni, S Polberg, R Booth, M Caminada & H Kido (eds), Computational Models of Argument - Proceedings of COMMA 2022. Frontiers in Artificial Intelligence and Applications, vol. 353, IOS Press, pp. 353-354. https://doi.org/10.3233/FAIA220169

URI

http://hdl.handle.net/1874/425267