BATMAN: BAyesian Target Modelling for Active iNference

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Samenvatting

Active Inference is an emerging framework for designing intelligent agents. In an Active Inference setting, any task is formulated as a variational free energy minimisation problem on a generative probabilistic model. Goal-directed behaviour relies on a clear specification of desired future observations. Learning desired observations would open up the Active Inference approach to problems where these are difficult to specify a priori. This paper introduces the BAyesian Target Modelling for Active iNference (BATMAN) approach, which augments an Active Inference agent with an additional, separate model that learns desired future observations from a separate data source. The main contribution of this paper is the design of a coupled generative model structure that facilitates learning desired future observations for Active Inference agents and supports integration of Active Inference and classical methods in a joint framework. We provide proof-of-concept validation for BATMAN through simulations.

Originele taal-2Engels
Titel2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's3852-3856
Aantal pagina's5
ISBN van elektronische versie978-1-5090-6631-5
ISBN van geprinte versie978-1-5090-6632-2
DOI's
StatusGepubliceerd - mei 2020
Evenement2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Virtual, Barcelona, Spanje
Duur: 4 mei 20208 mei 2020
Congresnummer: 2020
https://2020.ieeeicassp.org/

Congres

Congres2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Verkorte titelICASSP
LandSpanje
StadBarcelona
Periode4/05/208/05/20
Internet adres

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