Runtime Consultants

2025-08-03

AI Summary

This work addresses the challenge of dynamically enforcing safety constraints and optimizing quantitative objectives at runtime. We propose the “Runtime Advisor” paradigm—a proactive, decision-support mechanism that actively recommends next-step actions during system execution, supporting ω-regular properties and their quantitative semantics while adapting recommendations in real time based on observed execution traces. Methodologically, we integrate ω-automata theory with formal value-function computation to design the first constant-time advisor algorithm. For common verification scenarios, we construct concrete, correct, and efficient implementations tailored to representative quantitative value functions. Experimental evaluation demonstrates effectiveness under both Boolean and quantitative semantics, significantly improving runtime guidance capability and adaptability compared to reactive monitoring approaches.

Technology Category

• Search and Optimization: Adversarial Search
• Reasoning under Uncertainty: Sequential Decision Making
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

In this paper we introduce the notion of a runtime consultant. A runtime consultant is defined with respect to some value function on infinite words. Similar to a runtime monitor, it runs in parallel to an execution of the system and provides inputs at every step of the run. While a runtime monitor alerts when a violation occurs, the idea behind a consultant is to be pro-active and provide recommendations for which action to take next in order to avoid violation (or obtain a maximal value for quantitative objectives). It is assumed that a runtime-controller can take these recommendations into consideration. The runtime consultant does not assume that its recommendations are always followed. Instead, it adjusts to the actions actually taken (similar to a vehicle navigation system). We show how to compute a runtime consultant for common value functions used in verification, and that almost all have a runtime consultant that works in constant time. We also develop consultants for $ω$-regular properties, under both their classical Boolean semantics and their recently proposed quantitative interpretation.

Problem

• Defines runtime consultant for value functions on infinite words
• Provides proactive recommendations to avoid violations during execution
• Computes consultants for ω-regular properties under Boolean and quantitative semantics

Innovation

• Pro-active runtime consultant for system actions
• Adjusts recommendations based on actual actions
• Constant-time consultants for common value functions