🤖 AI Summary
Traditional intensional logics fail to capture trust’s hyperintensional sensitivity—i.e., its ability to distinguish semantically distinct but logically equivalent propositions—and cannot uniformly model agents’ cognitive attitudes alongside evidential support relations.
Method: We propose the first hyperintensional trust logic framework integrating quantitative epistemic logic with justification logic, featuring a non-standard identity mechanism and frame-based semantics to define an evidence-based belief-type trust operator.
Contribution/Results: (1) First formal characterization of trust’s hyperintensionality; (2) A sound, complete, and decidable axiomatic system; (3) A precise semantic definition and associated proof-theoretic tools. This work establishes a rigorous logical foundation for trust modeling in trustworthy AI and multi-agent systems.
📝 Abstract
We present a logical framework that enables us to define a formal theory of computational trust in which this notion is analysed in terms of epistemic attitudes towards the possible objects of trust and in relation to existing evidence in favour of the trustworthiness of these objects. The framework is based on a quantified epistemic and justification logic featuring a non-standard handling of identities. Thus, the theory is able to account for the hyperintensional nature of computational trust. We present a proof system and a frame semantics for the logic, we prove soundness and completeness results and we introduce the syntactical machinery required to define a theory of trust.