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This proceedings volume addresses the critical challenge of embedding formal reasoning systems into AI models to enhance trustworthiness and reliability through rule-based constraints. By integrating diverse programming paradigms with logical frameworks, these papers aim to bridge the gap between probabilistic learning and deterministic verification for safer autonomous agents. The work represents a significant step forward in aligning advanced machine learning capabilities with rigorous logic principles essential for high-stakes applications.
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Integrating Reasoning Systems for Trustworthy AI, Proceedings of the 4th Workshop on Logic and Practice of Programming (LPOP)

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arXiv:2410.19738v2 Announce Type: replace Abstract: This proceedings contains abstracts and position papers for the work to be presented at the fourth Logic and Practice of Programming (LPOP) Workshop. The workshop is to be held in Dallas, Texas, USA, and as a hybrid event, on October 13, 2024, in conjunction with the 40th International Conference on Logic Programming (ICLP). The focus of this workshop is integrating reasoning systems for trustworthy AI, especially including integrating diverse models of programming with rules and constraints.

Abstract:This proceedings contains abstracts and position papers for the work to be presented at the fourth Logic and Practice of Programming (LPOP) Workshop. The workshop is to be held in Dallas, Texas, USA, and as a hybrid event, on October 13, 2024, in conjunction with the 40th International Conference on Logic Programming (ICLP). The focus of this workshop is integrating reasoning systems for trustworthy AI, especially including integrating diverse models of programming with rules and constraints.

Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO); Programming Languages (cs.PL)

arXiv:2410.19738 [cs.AI]

(or arXiv:2410.19738v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2410.19738 Focus to learn more arXiv-issued DOI via DataCite

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