TL;DR
This work encodes a range of famous paradoxes in Answer Set Programming (ASP) using weak constraints, which abstraction and generalization provides a plain methodology for encoding normative systems in this language.
Making decisions despite conflicting norms and incomplete or unreliable information is a fundamental challenge for autonomous systems. We introduce a simple doxastic deontic logic for this setting: a classically reducible fragment of Chellas'Minimal Deontic Logic, extended with explicit conditional norms and combined w...
This work proposes to consider the three refined GAS principles as alternative principles for answer set semantics in general and for answer set and world view construction in particular and analyzes the computational complexity of well-supportedness and the rational answer set and world view semantics.
Artificial intelligence systems increasingly operate in normative domains where ethical and legal obligations must be formally represented. Standard Deontic Logic (SDL), a central reference point in formal treatments of normative reasoning, presupposes bivalence and deontic necessitation, and its structural commitments...
It is argued that indeterminacy, rather than correctness alone, is a central challenge for AI reasoning, and that state-of-the-art language models systematically fail to distinguish between determined and undetermined instances, exhibiting miscalibrated reasoning even in verification settings.
This paper points out general differences between the reasoning processes of ASP vs. conditionals and provides guidelines for knowledge engineering with ASP and conditionals, and illustrates how choosing either of the two frameworks has significant effects on the resulting solutions.
Recent progress in large language model reasoning has been driven by benchmarks and reinforcement learning environments with automatically verifiable rewards, particularly in mathematics, code, and formal logic. These settings make model accuracy easier to evaluate and optimize, but it remains unclear how far success u...
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