The Complexity of Non-Iterated Probabilistic Justification Logic

The logic PJ is a probabilistic logic defined by adding (noniterated) probability operators to the basic justification logic J. In this paper we establish upper and lower bounds for the complexity of the derivability problem in the logic PJ. The main result of the paper is that the complexity of the derivability problem in PJ remains the same as the complexity of the derivability problem in the underlying logic J, which is π[p/2] -complete. This implies that the probability operators do not increase the complexity of the logic, although they arguably enrich the expressiveness of the language.

Probabilistic Justification Logic

We present a probabilistic justification logic, PPJ, to study rational belief, degrees of belief and justifications. We establish soundness and completeness for PPJ and show that its satisfiability problem is decidable. In the last part we use PPJ to provide a solution to the lottery paradox.

Domain Globalization: Using Languages to Support Technical and Social Coordination

When a project is realized in a globalized environment, multiple stakeholders from different organizations work on the same system. Depending on the stakeholders and their organizations, various (possibly overlapping) concerns are raised in the development of the system. In this context a Domain Specific Language (DSL) supports the work of a group of stakeholders who are responsible for addressing a specific set of concerns. This chapter identifies the open challenges arising from the coordination of globalized domain-specific languages. We identify two types of coordination: technical coordination and social coordination. After presenting an overview of the current state of the art, we discuss first the open challenges arising from the composition of multiple DSLs, and then the open challenges associated to the collaboration in a globalized environment.

Propagation of Behavioral Variations with Delegation Proxies

Scoping behavioral variations to dynamic extents is useful to support non-functional concerns that otherwise result in cross-cutting code. Unfortunately, such forms of scoping are difficult to obtain with traditional reflection or aspects. We propose delegation proxies, a dynamic proxy model that supports behavioral intercession through the interception of various interpretation operations. Delegation proxies permit different behavioral variations to be easily composed together. We show how delegation proxies enable behavioral variations that can propagate to dynamic extents. We demonstrate our approach with examples of behavioral variations scoped to dynamic extents that help simplify code related to safety, reliability, and monitoring.