Temporal Justification Logic

Justification logics are modal-like logics with the additional capability of recording the reason, or justification, for modalities in syntactic structures, called justification terms. Justification logics can be seen as explicit counterparts to modal logics. The behavior and interaction of agents in distributed system is often modeled using logics of knowledge and time. In this paper, we sketch some preliminary ideas on how the modal knowledge part of such logics of knowledge and time could be replaced with an appropriate justification logic

Subset models for justification logic

We introduce a new semantics for justification logic based on subset relations. Instead of using the established and more symbolic interpretation of justifications, we model justifications as sets of possible worlds. We introduce a new justification logic that is sound and complete with respect to our semantics. Moreover, we present another variant of our semantics that corresponds to traditional justification logic. These types of models offer us a versatile tool to work with justifications, e.g.~by extending them with a probability measure to capture uncertain justifications. Following this strategy we will show that they subsume Artemov's approach to aggregating probabilistic evidence

A formal analysis of the notion of preference between deductive arguments

In the last two decades, justification logic has addressed the problem of including justifications into the field of epistemic logic. Nevertheless, there is something that has not received enough attention yet: how epistemic agents might prefer certain justifications to others, in order to have better pieces of evidence to support a particular belief. In this work, we study the notion of preference between a particular kind of justifications: deductive arguments. For doing so, we have built a logic using tools from epistemic logic, justification logic and logics for belief dependence. According to our solution, the preferences of an epistemic agent between different deductive arguments can be reduced to other notions

Probabilistic justification logic

We present a probabilistic justification logic, PPJ⁠, as a framework for uncertain reasoning about rational belief, degrees of belief and justifications. We establish soundness and strong completeness for PPJ with respect to the class of so-called measurable Kripke-like models and show that the satisfiability problem is decidable. We discuss how PPJ provides insight into the well-known lottery paradox