Active inference on discrete state-spaces: A synthesis

Journal of Mathematical Psychology, Journal Year: 2020, Volume and Issue: 99, P. 102447 - 102447

Published: Nov. 6, 2020

Active inference is a normative principle underwriting perception, action, planning, decision-making and learning in biological or artificial agents. From its inception, associated process theory has grown to incorporate complex generative models, enabling simulation of wide range behaviours. Due successive developments active inference, it often difficult see how underlying relates theories practical implementation. In this paper, we try bridge gap by providing complete mathematical synthesis on discrete state-space models. This technical summary provides an overview the theory, derives neuronal dynamics from first principles processes. Furthermore, paper fundamental building block needed understand for mixed models; allowing continuous sensations inform representations. may be used as follows: guide research towards outstanding challenges, implement simulate experimental behaviour, pointer various in-silico neurophysiological responses that make empirical predictions.

Language: Английский

---

The free energy principle made simpler but not too simple

Physics Reports, Journal Year: 2023, Volume and Issue: 1024, P. 1 - 29

Published: June 1, 2023

This paper provides a concise description of the free energy principle, starting from formulation random dynamical systems in terms Langevin equation and ending with Bayesian mechanics that can be read as physics sentience. It rehearses key steps using standard results statistical physics. These entail (i) establishing particular partition states based upon conditional independencies inherit sparsely coupled dynamics, (ii) unpacking implications this inference (iii) describing paths variational principle least action. Teleologically, offers normative account self-organisation optimal design decision-making, sense maximising marginal likelihood or model evidence. In summary, world systems, we end up sentient behaviour interpreted self-evidencing; namely, self-assembly, autopoiesis active inference.

Language: Английский

---

Predictive processing as a systematic basis for identifying the neural correlates of consciousness

Philosophy and the Mind Sciences, Journal Year: 2020, Volume and Issue: 1(II)

Published: Dec. 30, 2020

The search for the neural correlates of consciousness is in need a systematic, principled foundation that can endow putative with greater predictive and explanatory value. Here, we propose processing framework brain function as promising candidate providing this systematic foundation. proposal motivated by framework’s ability to address three general challenges identifying consciousness, satisfy two constraints common many theories consciousness. Implementing through lens delivers strong potential value detailed, mappings between substrates phenomenological structure. We conclude framework, precisely because it at outset not itself theory has significant advancing neuroscience

Language: Английский

---

Robot navigation as hierarchical active inference

Neural Networks, Journal Year: 2021, Volume and Issue: 142, P. 192 - 204

Published: May 10, 2021

Language: Английский

---

The evolution of brain architectures for predictive coding and active inference

Philosophical Transactions of the Royal Society B Biological Sciences, Journal Year: 2021, Volume and Issue: 377(1844)

Published: Dec. 27, 2021

This article considers the evolution of brain architectures for predictive processing. We argue that mechanisms perception and action are not late evolutionary additions advanced creatures like us. Rather, they emerged gradually from simpler loops (e.g. autonomic motor reflexes) were a legacy our earlier ancestors—and key to solving their fundamental problems adaptive regulation. characterize simpler-to-more-complex brains formally, in terms generative models include increasing hierarchical breadth depth. These may start simple homeostatic motif be elaborated during four main ways: these multimodal expansion control into an allostatic loop; its duplication form multiple sensorimotor expand animal's behavioural repertoire; gradual endowment with depth (to deal aspects world unfold at different spatial scales) temporal select plans future-oriented manner). In turn, elaborations underwrite solution biological regulation faced by increasingly sophisticated animals. Our proposal aligns neuroscientific theorising—about processing—with comparative data on animal species. is part theme issue ‘Systems neuroscience through lens theory’.

Language: Английский

---

World model learning and inference

Neural Networks, Journal Year: 2021, Volume and Issue: 144, P. 573 - 590

Published: Sept. 21, 2021

Understanding information processing in the brain-and creating general-purpose artificial intelligence-are long-standing aspirations of scientists and engineers worldwide. The distinctive features human intelligence are high-level cognition control various interactions with world including self, which not defined advance vary over time. challenge building human-like intelligent machines, as well progress brain science behavioural analyses, robotics, their associated theoretical formalisations, speaks to importance world-model learning inference. In this article, after briefly surveying history challenges internal model probabilistic learning, we introduce free energy principle, provides a useful framework within consider neuronal computation models. Next, showcase examples behaviour explained under that principle. We then describe symbol emergence context modelling, topic at frontiers cognitive robotics. Lastly, review recent by using novel programming languages. striking consensus emerges from these studies is descriptions inference powerful effective ways create machines understand how humans interact world.

Language: Английский

---

Designing ecosystems of intelligence from first principles

Collective Intelligence, Journal Year: 2024, Volume and Issue: 3(1)

Published: Jan. 1, 2024

This white paper lays out a vision of research and development in the field artificial intelligence for next decade (and beyond). Its denouement is cyber-physical ecosystem natural synthetic sense-making, which humans are integral participants—what we call “shared intelligence.” premised on active inference, formulation adaptive behavior that can be read as physics intelligence, inherits from self-organization. In this context, understand capacity to accumulate evidence generative model one’s sensed world—also known self-evidencing. Formally, corresponds maximizing (Bayesian) evidence, via belief updating over several scales, is, learning, selection. Operationally, self-evidencing realized (variational) message passing or propagation factor graph. Crucially, inference foregrounds an existential imperative intelligent systems; namely, curiosity resolution uncertainty. same underwrites sharing ensembles agents, certain aspects (i.e., factors) each agent’s world provide common ground frame reference. Active plays foundational role ecology sharing—leading formal account collective rests shared narratives goals. We also consider kinds communication protocols must developed enable such intelligences motivate hyper-spatial modeling language transaction protocol, first—and key—step towards ecology.

Language: Английский

---

Introducing ActiveInference.jl: A Julia Library for Simulation and Parameter Estimation with Active Inference Models

Entropy, Journal Year: 2025, Volume and Issue: 27(1), P. 62 - 62

Published: Jan. 12, 2025

We introduce a new software package for the Julia programming language, library ActiveInference.jl. To make active inference agents with Partially Observable Markov Decision Process (POMDP) generative models available to growing research community using Julia, we re-implemented pymdp Python. ActiveInference.jl is compatible cutting-edge libraries designed cognitive and behavioural modelling, as it used in computational psychiatry, science neuroscience. This means that POMDP can now be easily fit empirically observed behaviour sampling, well variational methods. In this article, show how makes building straightforward, enables researchers use them simulation, fitting data or performing model comparison.

Language: Английский

---

The Radically Embodied Conscious Cybernetic Bayesian Brain: From Free Energy to Free Will and Back Again

Entropy, Journal Year: 2021, Volume and Issue: 23(6), P. 783 - 783

Published: June 20, 2021

Drawing from both enactivist and cognitivist perspectives on mind, I propose that explaining teleological phenomena may require reappraising "Cartesian theaters" mental homunculi in terms of embodied self-models (ESMs), understood as body maps with agentic properties, functioning predictive-memory systems cybernetic controllers. Quasi-homuncular ESMs are suggested to constitute a major organizing principle for neural architectures due their initial ongoing significance solutions inference problems cognitive (and affective) development. Embodied experiences provide foundational lessons learning curriculums which agents explore increasingly challenging problem spaces, so answering an unresolved question Bayesian science: what biologically plausible mechanisms equipping learners sufficiently powerful inductive biases adequately constrain spaces? models neurophysiology, psychology, developmental robotics, describe how embodiment provides fundamental sources empirical priors (as reliably learnable posterior expectations). If play this kind role development, then bidirectional linkages will be found between all sensory modalities frontal-parietal control hierarchies, infusing senses somatic-motoric thereby structuring perception by relevant affordances, solving frame agents. upon the Free Energy Principle Active Inference framework, particular mechanism intentional action selection via consciously imagined explicitly represented) goal realization, where contrasts desired present states influence policy predictive coding backward-chained imaginings self-realizing predictions). This legacy suggests can intentionally shaped (internalized) partially-expressed motor acts, providing means attention, working memory, imagination, behavior. further nature(s) causation self-control, also account readiness potentials Libet paradigms wherein conscious intentions shape causal streams leading enaction. Finally, neurophenomenological handlings prototypical qualia including pleasure, pain, desire self-annihilating free energy gradients quasi-synesthetic interoceptive active inference. In brief, manuscript is intended illustrate radically minds create foundations intelligence capacity inference), consciousness somatically-grounded self-world modeling), deployment enacting valued goals).

Language: Английский

---

The Free Energy Principle for Perception and Action: A Deep Learning Perspective

Entropy, Journal Year: 2022, Volume and Issue: 24(2), P. 301 - 301

Published: Feb. 21, 2022

The free energy principle, and its corollary active inference, constitute a bio-inspired theory that assumes biological agents act to remain in restricted set of preferred states the world, i.e., they minimize their energy. Under this learn generative model world plan actions future will maintain agent an homeostatic state satisfies preferences. This framework lends itself being realized silico, as it comprehends important aspects make computationally affordable, such variational inference amortized planning. In work, we investigate tool deep learning design realize artificial based on presenting deep-learning oriented presentation surveying works are relevant both machine areas, discussing choices involved implementation process. manuscript probes newer perspectives for framework, grounding theoretical into more pragmatic affairs, offering practical guide newcomers starting point practitioners would like implementations principle.

Language: Английский

---