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Marco Zamponi

Publications and source records attributed to Marco Zamponi.

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STLSat---An Improved Tableau for Satisfiability Checking of Signal Temporal Logic Formulas

Signal Temporal Logic (STL) is a formalism used to describe temporal properties of real-valued signals in cyber-physical systems. In mission- and safety-critical domains, specifications often consist of large collections of STL formulas, making consistency checking and requirement analysis a major engineering bottleneck. Despite tableau-based satisfiability procedures being a natural solution to solve this problem, we have recently found out that the only existing tree-shaped tableau for bounded discrete-time STL does not provide a sound satisfiability/unsatisfiability verdict for all possible STL formulas. In this paper, we pinpoint the flaw in that procedure and present a new tree-shaped tableau which we prove to be sound and complete for bounded discrete-time STL. On top of this theoretical foundation, we introduce STLSat, an open-source Rust tool that decides the satisfiability of STL formulas, synthesizes concrete witness signals, checks the logical implication and equivalence between specifications, and extracts unsatisfiable cores, allowing users to identify inconsistent subsets of requirements for more effective specification debugging. STLSat also implements enhanced First-Order Logic and Satisfiability Modulo Theories encodings for STL, which allow it to act as a portfolio solver. We evaluate STLSat on an extended benchmark suite (including STL and Mission-time Linear Temporal Logic formulas) that we release publicly. Across the whole benchmark, the portfolio solver matches or outperforms state-of-the-art tools while preserving correctness guaranteed by our sound tableau procedure.

cs.LO

Certified Inductive Synthesis for Online Mixed-Integer Optimization

In fields such as autonomous and safety-critical systems, online optimization plays a crucial role in control and decision-making processes, often requiring the integration of continuous and discrete variables. These tasks are frequently modeled as mixed-integer programming (MIP) problems, where feedback data are incorporated as parameters. However, solving MIPs within strict time constraints is challenging due to their $\mathcal{NP}$-complete nature. A promising solution to this challenge involves leveraging the largely invariant structure of these problems to perform most computations offline, thus enabling efficient online solving even on platforms with limited hardware capabilities. In this paper we present a novel implementation of this strategy that uses counterexample-guided inductive synthesis to split the MIP solution process into two stages. In the offline phase, we construct a mapping that provides feasible assignments for binary variables based on parameter values within a specified range. In the online phase, we solve the remaining continuous part of the problem by fixing the binary variables to the values predicted by this mapping. Our numerical evaluation demonstrates the efficiency and solution quality of this approach compared to standard mixed-integer solvers, highlighting its potential for real-time applications in resource-constrained environments.

math.OC