Towards Quality-of-Service Metrics for Symbolic Knowledge Injection


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Andrea Agiollo, Andrea Rafanelli, Andrea Omicini

Angelo Ferrando, Viviana Mascardi (eds.)
CEUR Workshop Proceedings (AIxIA Series) 3261
Sun SITE Central Europe, RWTH Aachen University
November 2022

The integration of symbolic knowledge and sub-symbolic predictors represents a recent popular trend in AI. Among the set of integration approaches, Symbolic Knowledge Injection (SKI) proposes the exploitation of human-intelligible knowledge to steer sub-symbolic models towards some desired behaviour. The vast majority of works in the field of SKI aim at increasing the predictive performance of the sub-symbolic model at hand and, therefore, measure SKI strength solely based on performance improvements. However, a variety of artefacts exist that affect this measure, mostly linked to the quality of the injected knowledge and the underlying predictor. Moreover, the use of injection techniques introduces the possibility of producing more efficient sub-symbolic models in terms of computations, energy, and data required. Therefore, novel and reliable Quality-of-Service (QoS) measures for SKI are clearly needed, aiming at robustly identifying the overall quality of an injection mechanism. Accordingly, in this work, we propose and mathematically model the first – up to our knowledge – set of QoS metrics for SKI, focusing on measuring injection robustness and efficiency gain.

(keywords) symbolic knowledge injection, quality of service, efficiency, understandability, robustness

Talks

Journals & Series

Events

  • 23rd Workshop “From Objects to Agents” (WOA 2022) — 01/09/2022–03/09/2022

Publication

— authors

— editors

Angelo Ferrando, Viviana Mascardi

— status

published

— sort

paper in proceedings

— publication date

November 2022

— series

CEUR Workshop Proceedings / AIxIA Series

— volume

3261

— pages

30–47

— number of pages

18

URLs

original page  |  original PDF  |  open access PDF

identifiers

— DBLP

conf/woa/AgiolloRO22

— IRIS

11585/899383

— Scholar

5478874943780106057

— print ISSN

1613-0073

files

Open Access PDF

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