On the Design of PSyKE: A Platform for Symbolic Knowledge Extraction


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Federico Sabbatini, Giovanni Ciatto, Roberta Calegari, Andrea Omicini

Roberta Calegari, Giovanni Ciatto, Enrico Denti, Andrea Omicini, Giovanni Sartor (a cura di)
“WOA 2021 – 22nd Workshop “From Objects to Agents””, pp. 29–48
CEUR Workshop Proceedings (AI*IA Series) 2963
Sun SITE Central Europe, RWTH Aachen University
ottobre 2021

A common practice in modern explainable AI is to post-hoc explain black-box machine learning (ML) predictors – such as neural networks – by extracting symbolic knowledge out of them, in the form of either rule lists or decision trees. By acting as a surrogate model, the extracted knowledge aims at revealing the inner working of the black box, thus enabling its inspection, representation, and explanation.
Various knowledge-extraction algorithms have been presented in the literature so far. Unfortunately, running implementations of most of them are currently either proof of concepts or unavailable. In any case, a unified, coherent software framework supporting them all – as well as their interchange, comparison, and exploitation in arbitrary ML workflows – is currently missing.
Accordingly, in this paper we present the design of PSyKE, a platform providing general-purpose support to symbolic knowledge extraction from different sorts of black-box predictors via many extraction algorithms. Notably, PSyKE targets the extraction of symbolic knowledge in logic form, making it possible to extract first-order logic clauses as output. The extracted knowledge is thus both machine- and human- interpretable, and it can be used as a starting point for further symbolic processing—e.g. automated reasoning.

(keywords) explainable AI, knowledge extraction, interpretable prediction, PSyKE

Presentazioni

Eventi

  • 22nd Workshop “From Objects to Agents” (WOA 2021) — 01/09/2021–03/09/2021

Pubblicazioni

Pubblicazione

— autori/autrici

— a cura di

— stato

pubblicato

— tipo

articolo in atti

— data di pubblicazione

ottobre 2021

— volume

WOA 2021 – 22nd Workshop “From Objects to Agents”

— collana

CEUR Workshop Proceedings / AI*IA Series

— volume

2963

— pagine

29–48

— articolo n.

3

— numero di pagine

20

URL

pagina originale

identificatori

— DBLP

conf/woa/SabbatiniCCO21

— IRIS

11585/834364

— Scholar

879185583484020388

— Scopus

2-s2.0-85116894019

— print ISSN

1613-0073

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