scholarly journals Buddy System: An Adaptive Mental State Support System Based on Active Inference and Free Energy Principles

2021 ◽  
pp. 1-1
Author(s):  
Motoko Iwashita ◽  
Makiko Ishikawa
Keyword(s):  
Free Energy ◽  
Support System ◽  
Mental State ◽  
Active Inference ◽  
State Support ◽  
Buddy System ◽  
Energy Principles

SOCIO-ECONOMIC EFFICIENCY OF THE STATE SUPPORT SYSTEM FOR THE RUSSIAN FISHERIES SECTOR

2020 ◽  
pp. 104-110
Author(s):  
A.N. Stavtsev ◽  
Andrey Nikolaevich Osipov ◽  
Hatimat Nabievna Hasanova
Keyword(s):  
Economic Efficiency ◽  
Support System ◽  
The State ◽  
State Support

scholarly journals ‘Through others we become ourselves’: The dialectics of predictive coding and active inference

2019 ◽  
Author(s):  
Dimitris Bolis ◽  
Leonhard Schilbach
Keyword(s):  
Free Energy ◽  
Social Interaction ◽  
Predictive Coding ◽  
Real Life ◽  
Other Minds ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Multiscale Dynamics ◽  
Cultural Processes

Thinking Through Other Minds (TTOM) creatively situates the free energy principle within real-life cultural processes, thereby enriching both sociocultural theories and Bayesian accounts of cognition. Here, shifting the attention from thinking to becoming, we suggest complementing such an account by focusing on the empirical, computational and conceptual investigation of the multiscale dynamics of social interaction.

scholarly journals Natural language syntax complies with the free-energy principle

2021 ◽  
Author(s):  
Elliot Murphy ◽  
Emma Holmes ◽  
Karl Friston
Keyword(s):  
Free Energy ◽  
Natural Language ◽  
Explanatory Power ◽  
Speech Segmentation ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Language Functions ◽  
Theoretical Syntax ◽  
Natural Language Syntax

Natural language syntax yields an unbounded array of hierarchically structured expressions. We claim that these are used in the service of active inference in accord with the free-energy principle (FEP). While conceptual advances alongside modelling and simulation work have attempted to connect speech segmentation and linguistic communication with the FEP, we extend this program to the underlying computations responsible for generating elementary syntactic objects. We argue that recently proposed principles of economy in language design—such as “minimal search” and “least effort” criteria from theoretical syntax—adhere to the FEP. This permits a greater degree of explanatory power to the FEP—with respect to higher language functions—and presents linguists with a grounding in first principles of notions pertaining to computability. More generally, we explore the possibility of migrating certain topics in linguistics over to the domain of fields that investigate the FEP, such as complex polysemy. We aim to align concerns of linguists with the normative model for organic self-organisation associated with the FEP, marshalling evidence from theoretical linguistics and psycholinguistics to ground core principles of efficient syntactic computation within active inference.

scholarly journals Deep Active Inference and Scene Construction

2020 ◽  
Author(s):  
R. Conor Heins ◽  
M. Berk Mirza ◽  
Thomas Parr ◽  
Karl Friston ◽  
Igor Kagan ◽  
...  
Keyword(s):  
Free Energy ◽  
Sentence Comprehension ◽  
Perception And Action ◽  
Higher Order ◽  
Latent Structure ◽  
Active Inference ◽  
Perceptual Decision Making ◽  
Uncertain Environments ◽  
Evidence Accumulation ◽  
Scene Construction

AbstractAdaptive agents must act in intrinsically uncertain environments with complex latent structure. Here, we elaborate a model of visual foraging – in a hierarchical context – wherein agents infer a higher-order visual pattern (a ‘scene’) by sequentially sampling ambiguous cues. Inspired by previous models of scene construction – that cast perception and action as consequences of approximate Bayesian inference – we use active inference to simulate decisions of agents categorizing a scene in a hierarchically-structured setting. Under active inference, agents develop probabilistic beliefs about their environment, while actively sampling it to maximise the evidence for their internal generative model. This approximate evidence maximization (i.e. self-evidencing) comprises drives to both maximise rewards and resolve uncertainty about hidden states. This is realised via minimization of a free energy functional of posterior beliefs about both the world as well as the actions used to sample or perturb it, corresponding to perception and action, respectively. We show that active inference, in the context of hierarchical scene construction, gives rise to many empirical evidence accumulation phenomena, such as noise-sensitive reaction times and epistemic saccades. We explain these behaviours in terms of the principled drives that constitute the expected free energy, the key quantity for evaluating policies under active inference. In addition, we report novel behaviours exhibited by these active inference agents that furnish new predictions for research on evidence accumulation and perceptual decision-making. We discuss the implications of this hierarchical active inference scheme for tasks that require planned sequences of information-gathering actions to infer compositional latent structure (such as visual scene construction and sentence comprehension). Finally, we propose experiments to contextualise active inference in relation to other formulations of evidence accumulation (e.g. drift-diffusion models) in tasks that require planning in uncertain environments with higher-order structure.

scholarly journals Integrated World Modeling Theory (IWMT) Implemented: Towards Reverse Engineering Consciousness with the Free Energy Principle and Active Inference

2020 ◽  
Author(s):  
Adam Safron
Keyword(s):  
Free Energy ◽  
Reference Frames ◽  
Phenomenal Consciousness ◽  
Predictive Processing ◽  
Active Inference ◽  
Perceptual Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
World Modeling ◽  
Modeling Theory

Integrated World Modeling Theory (IWMT) is a synthetic model that attempts to unify theories of consciousness within the Free Energy Principle and Active Inference framework, with particular emphasis on Integrated Information Theory (IIT) and Global Neuronal Workspace Theory (GNWT). IWMT further suggests predictive processing in sensory hierarchies may be well-modeled as (folded, sparse, partially disentangled) variational autoencoders, with beliefs discretely-updated via the formation of synchronous complexes—as self-organizing harmonic modes (SOHMs)—potentially entailing maximal a posteriori (MAP) estimation via turbo coding. In this account, alpha-synchronized SOHMs across posterior cortices may constitute the kinds of maximal complexes described by IIT, as well as samples (or MAP estimates) from multimodal shared latent space, organized according to egocentric reference frames, entailing phenomenal consciousness as mid-level perceptual inference. When these posterior SOHMs couple with frontal complexes, this may enable various forms of conscious access as a kind of mental act(ive inference), affording higher order cognition/control, including the kinds of attentional/intentional processing and reportability described by GNWT. Across this autoencoding heterarchy, intermediate-level beliefs may be organized into spatiotemporal trajectories by the entorhinal/hippocampal system, so affording episodic memory, counterfactual imaginings, and planning.

scholarly journals Evolution of entrepreneurs state support system in the national aspect

2013 ◽  
Vol 7 (2) ◽  
pp. 206-215 ◽  
Author(s):  
Alena Shchemeleva ◽  
Daria Drozd
Keyword(s):  
Support System ◽  
State Support

scholarly journals Combining Active Inference and Hierarchical Predictive Coding: A Tutorial Introduction and Case Study

2019 ◽  
Author(s):  
Beren Millidge
Keyword(s):  
Free Energy ◽  
Reinforcement Learning ◽  
Predictive Coding ◽  
Action Selection ◽  
Proof Of Concept ◽  
Active Inference ◽  
Energy Principle ◽  
Free Energy Principle ◽  
Perceptual Model

This paper combines the active inference formulation of action (Friston, 2009) with hierarchical predictive coding models (Friston, 2003) to provide a proof-of-concept implementation of an active inference agent able to solve a common reinforcement learning baseline -- the cart-pole environment in OpenAI gym. It demonstrates empirically that predictive coding and active inference approaches can be successfully scaled up to tasks more challenging than the mountain car (Friston 2009, 2012). We show that hierarchical predictive coding models can be learned from scratch during the task, and can successfully drive action selection via active inference. To our knowledge, it is the first implemented active inference agent to combine active inference with a hierarchical predictive coding perceptual model. We also provide a tutorial walk-through of the free-energy principle, hierarchical predictive coding, and active inference, including an in-depth derivation of our agent.

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