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A symbolic-probabilistic framework that unites symbolic epistemic structure with probabilistic belief dynamics and supports a convergence guarantee and provides a diagnostic interpretation of the remaining disagreement is proposed.
A hybrid approach exploits the advantages of answer set programming in elaboration tolerance, declarative integrity constraints, and explanations, with the numerical flexibility of Python and provides formal guarantees of admissibility preservation under refinement, unique mass-preserving repair under coarsening, and e...
This study contributes to the field of artificial intelligence by offering a structured approach to building, testing, and refining multi-agent architectures that balance knowledge grounding, perspective modelling, and reasoning validation.
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