Managing caching strategies for stream reasoning with reinforcement learning. Issue 5 (September 2020)

• Record Type: Journal Article
• Authors: DODARO, CARMINE; EITER, THOMAS; OGRIS, PAUL; SCHEKOTIHIN, KONSTANTIN
• Is Part Of: Theory and practice of logic programming. Volume 20:Issue 5(2020)
• Page Start: 625
• Page End: 640
• Publication Date: 2020-09
• Subjects: Stream reasoning, -- ASP, -- Reinforcement learning, -- Caching strategies; Logic programming -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; Constraint programming (Computer science) -- Periodicals; 005.115
• DOI: 10.1017/S147106842000037X
• Languages: English
• ISSNs: 1471-0684

Abstract

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.