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The Mizar system is one of the pioneering systems aimed at supporting mathematical proof development on a computer that have laid the groundwork for and eventually have evolved into modern interactive proof assistants. We claim that an important milestone in the development of these systems was the creation of organized libraries accumulating all previously available formalized knowledge in such a way that new works could effectively re-use all previously collected notions. In the case of Mizar, the turning point of its development was the decision to start building the Mizar Mathematical Library as a centrally-managed knowledge base maintained together with the formalization language and the verification system. In this paper we show the process of forming this library, the evolution of its design principles, and also present some data showing its current use with the modern version of the Mizar proof checker, but also as a rich corpus of semantically linked mathematical data in various areas including web-based and natural language proof presentation, maths education, and machine learning based automated theorem proving.

Abstract. Enormous progress has been achieved in the last decade in the verification of timed systems, making it possible to analyze significant real-world protocols. An open challenge is the identification of fully symbolic verification techniques, able to deal effectively with the finite state component as well as with the timing aspects. In this paper we propose a new, symbolic verification technique that extends the Bounded Model Checking (BMC) approach for the verification of timed systems. The approach is based on the following ingredients. First, a BMC problem for timed systems is reduced to the satisfiability of a math-formula, i.e., a boolean combination of propositional variables and linear mathematical relations over real variables (used to represent clocks). Then, an appropriate solver, called MathSAT, is used to check the satisfiability of the math-formula. The solver is based on the integration of SAT techniques with some specialized decision procedures for linear mathematical constraints, and requires polynomial memory. Our methods allow for handling expressive properties in a fully-symbolic way. A preliminary experimental evaluation confirms the potential of the approach.