Active Inference
PulseAugur coverage of Active Inference — every cluster mentioning Active Inference across labs, papers, and developer communities, ranked by signal.
5 day(s) with sentiment data
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AI Agents Conceptualized as Hierarchical Belief Webs
This post introduces an informal framework for understanding intelligent agents as "belief webs." This model integrates concepts from active inference, agent foundations, and machine learning, proposing that beliefs, go…
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New theory of agency grounded in relational biology and process ontology
This paper, "A Matter of Time: Towards a General Theory of Agency," proposes a graded organizational theory of agency rooted in relational biology, physical biosemiotics, and process ontology. It argues that agency orig…
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Active Inference Controller Optimizes Traffic Signals in Challenging Environments
Researchers have developed an active inference controller for traffic signal management in noisy and unpredictable IoT environments. This controller dynamically selects signal phases by minimizing expected free energy, …
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New AI agent BRAIN enhances 6G network adaptability and explainability
Researchers have developed a new AI agent called BRAIN (Bayesian Reasoning via Active Inference) designed for future 6G mobile networks. This agent utilizes a deep generative model and active inference to unify percepti…
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Active inference framework clarified in new research paper
Researchers have published a paper detailing active inference, a framework that frames decision-making as a form of inference. The work clarifies how expected free energy minimization relates to variational free energy …
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AI agents need persistent internal states, not just on-demand processing
The current paradigm for AI agents, where they function like smart calculators that activate only when prompted, is a significant limitation. True AI agents require a persistent internal state and continuous operation, …
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New framework improves U-statistics with active inference for costly labels
Researchers have developed a new active inference framework for U-statistics, aiming to improve estimation efficiency when labeling data is expensive. This approach selectively queries informative labels within a fixed …
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Category theory framework proposed for defining and comparing AGI architectures
This working paper proposes a formal framework for comparing different Artificial General Intelligence (AGI) architectures using category theory. The authors aim to provide a unified foundation for AGI systems, integrat…