Ticker: A System for Incremental ASP-based Stream Reasoning

TL;DR

Ticker is a system that enables incremental, stream-based reasoning using ASP and LARS, allowing dynamic updates and model adjustments in real-time data environments.

Contribution

It introduces Ticker, a novel engine combining ASP, LARS, and truth maintenance for efficient incremental reasoning over streaming data.

Findings

Incremental updates improve reasoning efficiency.

Comparison shows Ticker's approach outperforms non-incremental methods.

Effective handling of dynamic, streaming data environments.

Abstract

In complex reasoning tasks, as expressible by Answer Set Programming (ASP), problems often permit for multiple solutions. In dynamic environments, where knowledge is continuously changing, the question arises how a given model can be incrementally adjusted relative to new and outdated information. This paper introduces Ticker, a prototypical engine for well-defined logical reasoning over streaming data. Ticker builds on a practical fragment of the recent rule-based language LARS which extends Answer Set Programming for streams by providing flexible expiration control and temporal modalities. We discuss Ticker's reasoning strategies: First, the repeated one-shot solving mode calls Clingo on an ASP encoding. We show how this translation can be incrementally updated when new data is streaming in or time passes by. Based on this, we build on Doyle's classic justification-based truth maintenance system (TMS) to update models of non-stratified programs. Finally, we empirically compare the obtained evaluation mechanisms. This paper is under consideration for acceptance in TPLP.