Progression- vs Automata-based Anticipatory Monitoring of LTL over Finite Traces (Extended Version)

2026-09-26

AI Summary

This study addresses the high computational complexity and difficulty in handling arithmetic constraints inherent in automata-based runtime verification for safety-critical systems with unknown specifications. Specifically, this work investigates anticipatory monitoring over finite traces under Linear Temporal Logic on finite traces (LTLf). A novel approach is proposed to overcome the bottlenecks of traditional automata construction by integrating a progress mechanism with LTLf satisfiability checking. Experimental results demonstrate that, in scenarios involving arithmetic constraints, the proposed method effectively produces verdicts, thereby resolving the non-termination issue characteristic of automata-based techniques. Furthermore, in propositional settings, this hybrid approach achieves a favorable performance trade-off, significantly enhancing both monitoring efficiency and practical feasibility.

Abstract

When safety-critical systems are developed from a known internal specification, their correctness can be established by model checking. In the frequent case where such a specification is unknown or inaccessible, runtime verification presents an attractive alternative, e.g., to ascertain that autonomous and agentic systems as well as business processes satisfy desirable properties and/or comply with safety requirements. In this paper we study anticipatory monitoring, an advanced form of runtime verification, where the monitoring state is determined by both the trace prefix seen so far, and all its possible finite-length, future continuations. We focus on monitoring linear-time properties that may involve arithmetic constraints. Automata-based approaches, the de-facto standard in this setting, are notorious for their computational complexity. We propose an alternative approach based on progression and LTLf satisfiability checking, for both propositional and arithmetic settings. We experimentally compare the automata- and progression-based approaches, and a third method that combines the two. Our experiments suggest that the progression-based approach often succeeds in producing a verdict when the automata constructions do not terminate, especially for the arithmetic setting. For the propositional setting, the combined technique provides a good tradeoff.

Problem

• Runtime Verification
• Anticipatory Monitoring
• LTLf
• Automata-based Monitoring
• Arithmetic Constraints

Innovation

• Anticipatory Monitoring
• Runtime Verification
• Progression-based Approach
• LTLf Satisfiability
• Automata-based Monitoring