5 articles tagged with #formal-logic. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
This academic paper proposes a neuro-symbolic approach for AGI robots combining neural networks with formal logic reasoning using Belnap's 4-valued logic system. The framework enables robots to handle unknown information, inconsistencies, and paradoxes while maintaining controlled security through axiom-based logic inference.
Researchers present CGD-PD, a test-time decoding method that improves large language models' performance on three-way logical question answering (True/False/Unknown) by enforcing negation consistency and resolving epistemic uncertainty through targeted entailment probes. The approach achieves up to 16% relative accuracy improvements on the FOLIO benchmark while reducing spurious Unknown predictions.
Researchers introduce Delta1, a framework that integrates automated theorem generation with large language models to create explainable AI reasoning. The system combines formal logic rigor with natural language explanations, demonstrating applications across healthcare, compliance, and regulatory domains.
Researchers introduce a new family of gradual semantics called aggregative semantics for Quantitative Bipolar Argumentation Frameworks (QBAF) in AI systems. The approach uses a three-stage computation that separately aggregates attackers and supporters before combining them with argument weights, providing more interpretable and parametrisable AI reasoning systems.
Researchers have developed an automation approach for Input/Output (I/O) Logics, a type of deontic logic used for reasoning about norms and obligations, by reducing them to propositional satisfiability problems. A prototype implementation called 'rio' (reasoner for input/output logics) has been created to demonstrate these procedures with practical examples.
