Constraint satisfaction problems involving strings have been a subject of theoretical study for decades, but the recent years have seen an increased interest in the development of practical solving methods. This interest in solving string constraints led to the development of various techniques and solvers, often accompanied by specific benchmark sets. As a result, there is now a substantial corpus of publicly available, yet largely unclassified, such benchmarks. In this context, we present SMTQuery , a framework for maintaining and analyzing benchmarks for SMT string problems. SMTQuery enables the execution of user-defined queries to extract domain-specific information from these benchmarks, facilitating a deeper analysis of the underlying problems. We demonstrate its utility by analyzing over 100,000 benchmarks and training an algorithm selection model to match benchmarks with suitable solvers.

Abstract The increased interest in string solving in the recent years has made it very hard to identify the right tool to address a particular user's purpose. Firstly, there is a multitude of string solvers, each addressing essentially some subset of the general problem. Generally, the addressed fragments are relevant and well motivated, but the lack of comparisons between the existing tools on an equal set of benchmarks cannot go unnoticed, especially as a common framework to compare solvers seems to be missing. In this paper, we gather a set of relevant benchmarks and introduce our new benchmarking framework to address this purpose.

Abstract Path planning and task scheduling are two challenging problems in the design of multiple autonomous agents. Both problems can be solved by the use of exhaustive search techniques such as model checking and algorithmic game theory. However, model checking suffers from the infamous state-space explosion problem that makes it inefficient at solving the problems when the number of agents is large, which is often the case in realistic scenarios. In this paper, we propose a new version of our novel approach called MCRL that integrates model checking and reinforcement learning to alleviate this scalability limitation. We apply this new technique to synthesize path planning and task scheduling strategies for multiple autonomous agents. Our method is capable of handling a larger number of agents if compared to what is feasibly handled by the model-checking technique alone. Additionally, MCRL also guarantees the correctness of the synthesis results via post-verification. The method is implemented in UPPAAL STRATEGO and leverages our tool MALTA for model generation, such that one can use the method with less effort of model construction and higher efficiency of learning than those of the original MCRL. We demonstrate the feasibility of our approach on an industrial case study: an autonomous quarry, and discuss the strengths and weaknesses of the methods.

Metric Temporal Logic MTL0,∞ is a timed extension of linear temporal logic, LTL, with time intervals whose left endpoints are zero or whose right endpoints are infinity. Whereas the satisfiability and model-checking problems for MTL0,∞ are both decidable, we note that the controller synthesis problem for MTL0,∞ is unfortunately undecidable. As a remedy of this we propose an approximate method to the synthesis problem, which we demonstrate to be adequate and scalable to practical examples. We define a method for converting MTL0,∞ formulas into (nondeterministic) Timed Game Büchi Automata and furthermore show how to construct determinized over- and underapproximation of a such. For the proposed method, we present a toolchain seamlessly integrating the needed components for practical MTL0,∞ synthesis. Lastly we demonstrate on a pair of case-studies the applicability and scalability of the proposed method.