AGWM improves world model accuracy in compositional environments by learning an explicit DAG of action affordance prerequisites to handle dynamic executability.
arXiv preprint arXiv:1910.01075 , year=
7 Pith papers cite this work, alongside 46 external citations. Polarity classification is still indexing.
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DAG-DC-ADMM jointly clusters subjects and learns their cluster-specific causal DAGs via structural equation modeling, groupwise truncated Lasso fusion penalties, and an ADMM solver for the resulting nonconvex problem.
DECHRL models causal structures and stochastic delay distributions within hierarchical RL and incorporates them into a delay-aware empowerment objective to improve performance under temporal uncertainty.
POSCMs extend SCMs to settings where the causal graph itself is generated by latent context and can be intervened on, with conditional kernel-identifiability theorems and illustrative retina simulations.
Introduces progressive visualization for comparing causal discovery algorithms and comparative graph layouts for analyzing multi-outcome causal graphs in healthcare.
Empirical evaluation on synthetic and real-world datasets indicates that natural experiments are present and can be leveraged via causal feature selection to boost model performance.
A survey of physical AI that distinguishes theoretical physics reasoning from applied understanding and synthesizes advances in symbolic reasoning, embodied systems, and generative models to advocate for physics-grounded world models.
citing papers explorer
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AGWM: Affordance-Grounded World Models for Environments with Compositional Prerequisites
AGWM improves world model accuracy in compositional environments by learning an explicit DAG of action affordance prerequisites to handle dynamic executability.
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A Unified Framework for Structure-Aware Clustering and Heterogeneous Causal Graph Learning
DAG-DC-ADMM jointly clusters subjects and learns their cluster-specific causal DAGs via structural equation modeling, groupwise truncated Lasso fusion penalties, and an ADMM solver for the resulting nonconvex problem.
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Delay-Empowered Causal Hierarchical Reinforcement Learning
DECHRL models causal structures and stochastic delay distributions within hierarchical RL and incorporates them into a delay-aware empowerment objective to improve performance under temporal uncertainty.
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Partially Observed Structural Causal Models
POSCMs extend SCMs to settings where the causal graph itself is generated by latent context and can be intervened on, with conditional kernel-identifiability theorems and illustrative retina simulations.
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Visual Analysis of Multi-outcome Causal Graphs
Introduces progressive visualization for comparing causal discovery algorithms and comparative graph layouts for analyzing multi-outcome causal graphs in healthcare.
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Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection
Empirical evaluation on synthetic and real-world datasets indicates that natural experiments are present and can be leveraged via causal feature selection to boost model performance.
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Aligning Perception, Reasoning, Modeling and Interaction: A Survey on Physical AI
A survey of physical AI that distinguishes theoretical physics reasoning from applied understanding and synthesizes advances in symbolic reasoning, embodied systems, and generative models to advocate for physics-grounded world models.