# On-the-Fly Construction of Composite Events in Scenario-Based Modeling using Constraint Solvers

arXiv: https://arxiv.org/abs/1909.00408 · HTML: https://mlsanity.com/paper/1909.00408/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [Formally Verifying Deep Reinforcement Learning Controllers with Lyapunov Barrier Certificates](https://mlsanity.com/paper/2405.14058/) (2024)
- [Scenario-Assisted Deep Reinforcement Learning](https://mlsanity.com/paper/2202.04337/) (2022)
- [Enhancing Deep Learning with Scenario-Based Override Rules: a Case Study](https://mlsanity.com/paper/2301.08114/) (2023)
- [On Integrating Large Language Models and Scenario-Based Programming for Improving Software Reliability](https://mlsanity.com/paper/2509.09194/) (2025)

## Abstract (arXiv)

Scenario-Based Programming is a methodology for modeling and constructing complex reactive systems from simple, stand-alone building blocks, called scenarios. These scenarios are designed to model different traits of the system, and can be interwoven together and executed to produce cohesive system behavior. Existing execution frameworks for scenario-based programs allow scenarios to specify their view of what the system must, may, or must not do only through very strict interfaces. This limits the methodology's expressive power and often prevents users from modeling certain complex requirements. Here, we propose to extend Scenario-Based Programming's execution mechanism to allow scenarios to specify how the system should behave using rich logical constraints. We then leverage modern constraint solvers (such as SAT or SMT solvers) to resolve these constraints at every step of running the system, towards yielding the desired overall system behavior. We provide an implementation of our approach and demonstrate its applicability to various systems that could not be easily modeled in an executable manner by existing Scenario-Based approaches.