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Simulating Active Inference Processes by Message Passing

The free energy principle (FEP) offers a variational calculus-based description for how biological agents persevere through interactions with their environment. Active inference (AI) is a corollary of the FEP, which states that biological agents act to fulfill prior beliefs about preferred future ob...

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Detalles Bibliográficos
Autores principales: van de Laar, Thijs W., de Vries, Bert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805795/
https://www.ncbi.nlm.nih.gov/pubmed/33501036
http://dx.doi.org/10.3389/frobt.2019.00020
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author van de Laar, Thijs W.
de Vries, Bert
author_facet van de Laar, Thijs W.
de Vries, Bert
author_sort van de Laar, Thijs W.
collection PubMed
description The free energy principle (FEP) offers a variational calculus-based description for how biological agents persevere through interactions with their environment. Active inference (AI) is a corollary of the FEP, which states that biological agents act to fulfill prior beliefs about preferred future observations (target priors). Purposeful behavior then results from variational free energy minimization with respect to a generative model of the environment with included target priors. However, manual derivations for free energy minimizing algorithms on custom dynamic models can become tedious and error-prone. While probabilistic programming (PP) techniques enable automatic derivation of inference algorithms on free-form models, full automation of AI requires specialized tools for inference on dynamic models, together with the description of an experimental protocol that governs the interaction between the agent and its simulated environment. The contributions of the present paper are two-fold. Firstly, we illustrate how AI can be automated with the use of ForneyLab, a recent PP toolbox that specializes in variational inference on flexibly definable dynamic models. More specifically, we describe AI agents in a dynamic environment as probabilistic state space models (SSM) and perform inference for perception and control in these agents by message passing on a factor graph representation of the SSM. Secondly, we propose a formal experimental protocol for simulated AI. We exemplify how this protocol leads to goal-directed behavior for flexibly definable AI agents in two classical RL examples, namely the Bayesian thermostat and the mountain car parking problems.
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spelling pubmed-78057952021-01-25 Simulating Active Inference Processes by Message Passing van de Laar, Thijs W. de Vries, Bert Front Robot AI Robotics and AI The free energy principle (FEP) offers a variational calculus-based description for how biological agents persevere through interactions with their environment. Active inference (AI) is a corollary of the FEP, which states that biological agents act to fulfill prior beliefs about preferred future observations (target priors). Purposeful behavior then results from variational free energy minimization with respect to a generative model of the environment with included target priors. However, manual derivations for free energy minimizing algorithms on custom dynamic models can become tedious and error-prone. While probabilistic programming (PP) techniques enable automatic derivation of inference algorithms on free-form models, full automation of AI requires specialized tools for inference on dynamic models, together with the description of an experimental protocol that governs the interaction between the agent and its simulated environment. The contributions of the present paper are two-fold. Firstly, we illustrate how AI can be automated with the use of ForneyLab, a recent PP toolbox that specializes in variational inference on flexibly definable dynamic models. More specifically, we describe AI agents in a dynamic environment as probabilistic state space models (SSM) and perform inference for perception and control in these agents by message passing on a factor graph representation of the SSM. Secondly, we propose a formal experimental protocol for simulated AI. We exemplify how this protocol leads to goal-directed behavior for flexibly definable AI agents in two classical RL examples, namely the Bayesian thermostat and the mountain car parking problems. Frontiers Media S.A. 2019-03-28 /pmc/articles/PMC7805795/ /pubmed/33501036 http://dx.doi.org/10.3389/frobt.2019.00020 Text en Copyright © 2019 van de Laar and de Vries. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Robotics and AI
van de Laar, Thijs W.
de Vries, Bert
Simulating Active Inference Processes by Message Passing
title Simulating Active Inference Processes by Message Passing
title_full Simulating Active Inference Processes by Message Passing
title_fullStr Simulating Active Inference Processes by Message Passing
title_full_unstemmed Simulating Active Inference Processes by Message Passing
title_short Simulating Active Inference Processes by Message Passing
title_sort simulating active inference processes by message passing
topic Robotics and AI
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805795/
https://www.ncbi.nlm.nih.gov/pubmed/33501036
http://dx.doi.org/10.3389/frobt.2019.00020
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