Managing caching strategies for stream reasoning with reinforcement learning

TL;DR

This paper introduces a reinforcement learning-based method to manage caching strategies in stream reasoning, leveraging conflict-driven constraint learning to improve performance in real-world reconfiguration problems.

Contribution

It proposes a novel approach combining CDCL with reinforcement learning to efficiently update solutions in stream reasoning systems, addressing expressiveness limitations of prior methods.

Findings

Significant performance improvements with learned constraints

Effective application of RL in stream reasoning scenarios

Enhanced handling of constraints in incremental reasoning

Abstract

Efficient decision-making over continuously changing data is essential for many application domains such as cyber-physical systems, industry digitalization, etc. Modern stream reasoning frameworks allow one to model and solve various real-world problems using incremental and continuous evaluation of programs as new data arrives in the stream. Applied techniques use, e.g., Datalog-like materialization or truth maintenance algorithms to avoid costly re-computations, thus ensuring low latency and high throughput of a stream reasoner. However, the expressiveness of existing approaches is quite limited and, e.g., they cannot be used to encode problems with constraints, which often appear in practice. In this paper, we suggest a novel approach that uses the Conflict-Driven Constraint Learning (CDCL) to efficiently update legacy solutions by using intelligent management of learned constraints. In particular, we study the applicability of reinforcement learning to continuously assess the utility of learned constraints computed in previous invocations of the solving algorithm for the current one. Evaluations conducted on real-world reconfiguration problems show that providing a CDCL algorithm with relevant learned constraints from previous iterations results in significant performance improvements of the algorithm in stream reasoning scenarios. Under consideration for acceptance in TPLP.