Belief Revision and Trust

TL;DR

This paper presents a formal framework for modeling trust in belief revision, using state-partitions and pseudometrics to incorporate domain-specific trust and multiple agents' reports effectively.

Contribution

It introduces a novel approach combining state-partitions and pseudometrics to model trust and trust degrees in belief revision processes.

Findings

Trust can be modeled with state-partitions per agent.

Pseudometrics enable comparison of trust levels across agents.

The approach effectively incorporates multiple agents' reports based on trust.

Abstract

Belief revision is the process in which an agent incorporates a new piece of information together with a pre-existing set of beliefs. When the new information comes in the form of a report from another agent, then it is clear that we must first determine whether or not that agent should be trusted. In this paper, we provide a formal approach to modeling trust as a pre-processing step before belief revision. We emphasize that trust is not simply a relation between agents; the trust that one agent has in another is often restricted to a particular domain of expertise. We demonstrate that this form of trust can be captured by associating a state-partition with each agent, then relativizing all reports to this state partition before performing belief revision. In this manner, we incorporate only the part of a report that falls under the perceived domain of expertise of the reporting agent. Unfortunately, state partitions based on expertise do not allow us to compare the relative strength of trust held with respect to different agents. To address this problem, we introduce pseudometrics over states to represent differing degrees of trust. This allows us to incorporate simultaneous reports from multiple agents in a way that ensures the most trusted reports will be believed.