On the role of Computational Logic in Data Science: representing, learning, reasoning, and explaining knowledge

Ciatto, Giovanni (2022) On the role of Computational Logic in Data Science: representing, learning, reasoning, and explaining knowledge, [Dissertation thesis], Alma Mater Studiorum Università di Bologna. Dottorato di ricerca in Data science and computation, 33 Ciclo. DOI 10.48676/unibo/amsdottorato/10192.
Documenti full-text disponibili:
[img] Documento PDF (English) - Richiede un lettore di PDF come Xpdf o Adobe Acrobat Reader
Disponibile con Licenza: Creative Commons Attribution Non-commercial No Derivatives 4.0 (CC BY-NC-ND 4.0) .
Download (14MB)


In this thesis we discuss in what ways computational logic (CL) and data science (DS) can jointly contribute to the management of knowledge within the scope of modern and future artificial intelligence (AI), and how technically-sound software technologies can be realised along the path. An agent-oriented mindset permeates the whole discussion, by stressing pivotal role of autonomous agents in exploiting both means to reach higher degrees of intelligence. Accordingly, the goals of this thesis are manifold. First, we elicit the analogies and differences among CL and DS, hence looking for possible synergies and complementarities along 4 major knowledge-related dimensions, namely representation, acquisition (a.k.a. learning), inference (a.k.a. reasoning), and explanation. In this regard, we propose a conceptual framework through which bridges these disciplines can be described and designed. We then survey the current state of the art of AI technologies, w.r.t. their capability to support bridging CL and DS in practice. After detecting lacks and opportunities, we propose the notion of logic ecosystem as the new conceptual, architectural, and technological solution supporting the incremental integration of symbolic and sub-symbolic AI. Finally, we discuss how our notion of logic ecosys- tem can be reified into actual software technology and extended towards many DS-related directions.

Tipologia del documento
Tesi di dottorato
Ciatto, Giovanni
Dottorato di ricerca
Settore disciplinare
Settore concorsuale
Parole chiave
Computational Logic, Data Science, XAI, Logic Ecosystem, 2P-Kt
Data di discussione
16 Giugno 2022

Altri metadati

Statistica sui download

Gestione del documento: Visualizza la tesi