arXiv AI
Jul 24
How Rules Represent Causal Knowledge: Causal Modeling with Probabilistic Logic Programming
arXiv:2607. 21208v1 Announce Type: new Abstract: Pearl famously argues that causal knowledge enables the prediction of intervention effects.
Logic Programming Semantics for Causal Processes
arXiv:2607. 21233v1 Announce Type: new Abstract: Motivated by challenging modelling issues in the life sciences, we investigate the relationship between logic programming semantics and the eventual states of causal processes compatible with those logic programs.
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Hugging Face Trending Papers
Jul 23
How Rules Represent Causal Knowledge: Causal Modeling with Probabilistic Logic Programming
Pearl famously argues that causal knowledge enables the prediction of intervention effects. By contrast, purely descriptive knowledge supports only conclusions drawn from observations.
arXiv AI
Aug 10
From probability to causality in probabilistic logic programming
arXiv:2608. 07230v1 Announce Type: new Abstract: Probabilistic logic programming is a formalism of statistical relational artificial intelligence that supports causal queries, including interventions from outside the system.
arXiv AI
Jul 31
Property-driven Causal Abstractions for Markov Decision Processes
arXiv:2607. 26787v1 Announce Type: new Abstract: Markov Decision Processes (MDPs) are widely used as decision-making models, commonly specified over factored state spaces through state variables and their valuations.
arXiv AI
Jul 1
Meta-Programming for Linear-time Temporal Answer Set Programming
arXiv:2605. 29965v2 Announce Type: replace Abstract: The development of temporal extensions of Answer Set Programming (ASP) has led to the emergence of non-monotonic linear-time (TEL), dynamic (DEL), and metric (MEL) temporal equilibrium logics.
arXiv AI
Jul 24
A New Well-Supported Semantics for Description Logic Programs
arXiv:2607. 21203v1 Announce Type: new Abstract: Description logic programs are a powerful formalism for combining rules with ontologies.
Hugging Face Trending Papers
Jul 30
Orca: Neural Operators for Causal Reasoning in Continuous Time
Structural causal models are the standard language for reasoning about interventions and counterfactuals, but they describe static variables, typically measured once, and usually forbid cyclic dependencies. Many systems we care about, such as patients, climates, and economies, instead evolve continuously in time, are observed at irregular time points, and contain feedback loops.
arXiv Machine Learning
Jul 28
Beyond Directed Acyclic Graphs: Causal Zeros and Causal Differential Equations
arXiv:2607. 22910v1 Announce Type: new Abstract: Pearl's structural causal model (SCM) framework, built on directed acyclic graphs (DAGs) and the do-calculus, is the dominant formal language for causal reasoning.
arXiv AI
Jul 24
Hybrid MKNF with Classical Negation in the Rule Component
arXiv:2607. 21202v1 Announce Type: cross Abstract: Hybrid MKNF knowledge bases under the well-founded semantics integrate Description Logics with Logic Programming.
Hugging Face Trending Papers
Jul 23
A New Well-Supported Semantics for Description Logic Programs
Description logic programs are a powerful formalism for combining rules with ontologies. The well-supported semantics for description logic programs ensures that no answer sets rely on cyclic dependencies.
arXiv AI
Jun 30
Sample-Efficient Learning of Probabilistic Causes for Reachability in Markov Decision Processes with Probabilistic Guarantees
arXiv:2606. 29681v1 Announce Type: new Abstract: Probabilistic model checking for Markov decision processes (MDPs) provides quantitative guarantees, but often offers limited insight into why undesired outcomes occur.
arXiv AI
Jul 28
Intuitionistic $j$-Do-Calculus in Topos Causal Models
arXiv:2510. 17944v2 Announce Type: replace-cross Abstract: In this paper, we generalize Pearl's do-calculus to an Intuitionistic setting called $j$-stable causal inference inside a topos of sheaves.