Modular Design Patterns for Systems that Learn and Reason

Abstract

The combination of data-driven techniques from machine learning with symbolic techniques from knowledge representation is recognised as one of the grand challenges of modern AI. We propose a set of compositional design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from knowledge representation. As in other areas of computer science (knowledge engineering, software engineering, ontology engineering, process mining and others), such design patterns help to systematize the literature, clarify which combinations of techniques serve which purposes, and encourage re-use of software components. We have validated our set of compositional design patterns against a large body of recent literature.