Motivated by the search for forms of distributed belief that do not collapse in the face of conflicting information, this paper introduces the notions of cautious and bold distributed belief. Both notions rely on maximally consistent subgroups of agents, with cautious quantifying universally and bold quantifying existentially. As a result, while the cautious distributed belief of a group is inconsistent only when all group members are individually inconsistent, the bold distributed belief of a group is never inconsistent. The paper discusses these two notions, presenting their respective modalities and semantic interpretations, discussing some of their basic properties, studying whether they preserve doxastic properties from the members of the group, and comparing them not only with standard distributed belief but also with one another, both at the level of modalities and at the level of language expressivity.

Motivated by the search for forms of distributed belief that do not collapse in the face of conflicting information, this paper introduces the notions of cautious and bold distributed belief. Both notions rely on maximally consistent subgroups of agents, with cautious quantifying universally and bold quantifying existentially. As a result, while the cautious distributed belief of a group is inconsistent only when all group members are individually inconsistent, the bold distributed belief of a group is never inconsistent. The paper discusses these two notions, presenting their respective modalities and semantic interpretations, discussing some of their basic properties, studying whether they preserve doxastic properties from the members of the group, and comparing them not only with standard distributed belief but also with one another, both at the level of modalities and at the level of language expressivity.

Explainable Agency (XAg) is a subfield of Explainable AI (XAI) dedicated to explaining agents that perceive, reflect, reason, and act on an environment. However, the process of decision that brings about action is very heterogeneous across different architectures, and thus results in similarly heterogeneous ways of producing explanations. This becomes an issue in the absence of standards for evaluating explanations, both whether they are understandable by humans, and also whether they are trustworthy: the paradigm of agent explainability is riddled with different definitions on what are good metrics for evaluating explanations of an agent of a particular architecture or agent paradigm. Furthermore, when those metrics require of user studies, much work needs to be redone when applied to a different agent architecture, cohort of explainees, or context. Within this work, we propose a unifying way of examining agent behaviour, stratified into different abstraction levels (the rungs of a ladder), so as to be able to compare different agent architecture components. This architecture serves as an engineering pattern, decoupling the problem of providing trustworthy abstractions for giving explanations from the problem of formulating those explanations in an understandable manner. This paper is a first step in bringing about a world where architectures provide – and evaluate their trustworthiness on – abstractions that have been tested and evaluated by user studies, as well as enabling explanation providers to innovate on methods for formulating explanations from abstractions without needing expert knowledge on every agent architecture.