Practical Aspects of Declarative Languages (eBook, PDF)

27th International Symposium, PADL 2025, Denver, CO, USA, January 20-21, 2025, Proceedings
Redaktion: Erdem, Esra; Vidal, Germán

• Format: PDF

Produktbeschreibung

This book constitutes the refereed proceedings of the 27th International Symposium on Practical Aspects of Declarative Languages, PADL 2025, held in Denver, CO, USA, during January 20-21, 2025.

The 15 full papers included in this book were carefully reviewed and selected from 26 submissions. The accepted papers span a range of topics related to functional and logic programming, including some novel applications of Answer Set Programming, language extensions, runtime monitoring, program transformations, type-checking, and applications of declarative programming techniques to artificial intelligence and machine learning, among others.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 249
• Erscheinungstermin: 17. März 2025
• Englisch
• ISBN-13: 9783031849244
• Artikelnr.: 73666456

Inhaltsangabe

.- On Bridging Prolog and Python to Enhance an Inductive Logic Program-ming System.

.- Type-Checking Heterogeneous Sequences in a Simple Embeddable Type System.

.- The Scenic Route to Deforestation.

.- MOLA: A Runtime Verification Engine Factory by (Meta-)interpreting Em-bedded DSLs.

.- SM-based Semantics for Answer Set Programs Containing Conditional Lit-erals and Arithmetic.

.- A practical approach to handling tabular data in logic.

.- Automated Playing of Survival Video Games with Commonsense Reasoning.

.- Checking Concurrency Coding Rules.

.- A Weighted Bipolar Argumentation Framework and its ASP-based Imple-mentation.

.- Haskell Based Spreadsheets.

.- Leveraging LLM Reasoning with Dual Horn Programs.

.- ASP for Language Documentation and Reclamation: A Derivational Stem-ming Tool for Myaamia.

.- Enhancing a Hierarchical Graph Rewriting Language based on MELL CutElimination.

.- C3G: Causally Constrained Counterfactual Generation.

.- Exploring Answer Set Programming for Provenance Graph-Based CyberThreat Detection: A Novel Approach.