BATMAN: BAyesian Target Modelling for Active iNference

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Abstract

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.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
Pages3852-3856
Number of pages5
ISBN (Electronic)9781509066315
DOIs
Publication statusPublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Virtual, Barcelona, Spain
Duration: 4 May 20208 May 2020
Conference number: 2020
https://2020.ieeeicassp.org/

Conference

Conference2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Abbreviated titleICASSP
CountrySpain
CityBarcelona
Period4/05/208/05/20
Internet address

Keywords

  • Active Inference
  • Adaptive Agents
  • Bayesian
  • Graphical Models
  • variational inference

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