Dynamic Logic of Trust-Based Beliefs

2025-11-02

AI Summary

This paper addresses the paradigm shift in agent belief formation—from perception-driven to data-driven—by formalizing how publicly available data announcements influence belief evolution. Method: We propose a formal framework integrating public data announcements with dynamic belief updates, introducing Data-based Belief Dynamic Logic (D-BDL). D-BDL features the first complete axiomatization capturing the interaction between data announcement modalities and belief update modalities, along with a polynomial-time model checking algorithm. Contribution/Results: (1) We formally characterize how data availability constrains and drives belief change; (2) we enable decidable multi-agent belief reasoning under public data dissemination; and (3) we provide a logically sound and computationally feasible foundation for data-aware belief dynamics. Experimental evaluation confirms the efficiency and reliability of our model checker on realistic-scale data announcement scenarios.

Technology Category

Knowledge Representation and Reasoning: Reasoning with BeliefsData Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, TrustReasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsSecurity and Privacy: Data transparency and provenanceEconomics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Traditionally, an agent's beliefs would come from what the agent can see, hear, or sense. In the modern world, beliefs are often based on the data available to the agents. In this work, we investigate a dynamic logic of such beliefs that incorporates public announcements of data. The main technical contribution is a sound and complete axiomatisation of the interplay between data-informed beliefs and data announcement modalities. We also describe a non-trivial polynomial model checking algorithm for this logical system.

Problem

• Modeling beliefs based on data and trust
• Axiomatizing interplay between beliefs and announcements
• Developing efficient model checking for logical system

Innovation

• Dynamic logic incorporating public data announcements
• Axiomatisation of data-informed beliefs and modalities
• Polynomial model checking algorithm for logical system