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. Frank van Harmelen proposes a set of compositional design patterns to describe a large variety of systems.
Date: 07 APRIL 2021 from 18:00 to 20:00
Event location: Teams
Modular design patterns for systems that learn and reason
https://arxiv.org/abs/2102.11965
Date: Wednesday April 7th at 6pm (45 mins presentation + 45 mins discussion)
The event will be held on Teams: Click here to join the meeting
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.