A SAT-Based Encoding of the One-Pass and Tree-Shaped Tableau System for LTL

A new one-pass and tree-shaped tableau system for LTL sat- isfiability checking has been recently proposed, where each branch can be explored independently from others and, furthermore, directly cor- responds to a potential model of the formula. Despite its simplicity, it proved itself to be effective in practice. In this paper, we provide a SAT-based encoding of such a tableau system, based on the technique of bounded satisfiability checking. Starting with a single-node tableau, i.e., depth k of the tree-shaped tableau equal to zero, we proceed in an incremental fashion. At each iteration, the tableau rules are encoded in a Boolean formula, representing all branches of the tableau up to the current depth k. A typical downside of such bounded techniques is the effort needed to understand when to stop incrementing the bound, to guarantee the completeness of the procedure. In contrast, termination and completeness of the proposed algorithm is guaranteed without com- puting any upper bound to the length of candidate models, thanks to the Boolean encoding of the PRUNE rule of the original tableau system. We conclude the paper by describing a tool that implements our procedure, and comparing its performance with other state-of-the-art LTL solvers

A Landscape of First-Order Linear Temporal Logics in Infinite-State Verification and Temporal Ontologies

We provide an overview of the main attempts to formalize and reason about the evolution over time of complex domains, through the lens of first-order temporal logics. Different communities have studied similar problems for decades, and some unification of concepts, problems and formalisms is a much needed but not simple task

Practical Reasoning in DatalogMTL

DatalogMTL is an extension of Datalog with metric temporal operators that has found an increasing number of applications in recent years. Reasoning in DatalogMTL is, however, of high computational complexity, which makes reasoning in modern data-intensive applications challenging. In this paper we present a practical reasoning algorithm for the full DatalogMTL language, which we have implemented in a system called MeTeoR. Our approach effectively combines an optimised (but generally non-terminating) materialisation (a.k.a. forward chaining) procedure, which provides scalable behaviour, with an automata-based component that guarantees termination and completeness. To ensure favourable scalability of the materialisation component, we propose a novel semina\"ive materialisation procedure for DatalogMTL enjoying the non-repetition property, which ensures that each specific rule application will be considered at most once throughout the entire execution of the algorithm. Moreover, our materialisation procedure is enhanced with additional optimisations which further reduce the number of redundant computations performed during materialisation by disregarding rules as soon as it is certain that they cannot derive new facts in subsequent materialisation steps. Our extensive evaluation supports the practicality of our approach.Comment: Under consideration in Theory and Practice of Logic Programming (TPLP). arXiv admin note: text overlap with arXiv:2208.0710