tag : eXplainable AI

Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Review (ACM Computing Surveys, 2024) — Giovanni Ciatto, Federico Sabbatini, Andrea Agiollo, Matteo Magnini, Andrea Omicini

Symbolic Knowledge Extraction for Explainable Nutritional Recommenders (Computer Methods and Programs in Biomedicine, 2023) — Matteo Magnini, Giovanni Ciatto, Furkan Cantürk, Reyhan Aydoǧan, Andrea Omicini

Knowledge injection of Datalog rules via Neural Network Structuring with KINS (Journal of Logic and Computation, 2023) — Matteo Magnini, Giovanni Ciatto, Andrea Omicini

Symbolic knowledge extraction from opaque ML predictors in PSyKE: Platform design & experiments (Intelligenza Artificiale, 2022) — Federico Sabbatini, Giovanni Ciatto, Roberta Calegari, Andrea Omicini

Preface (WOA 2021 – 22nd Workshop “From Objects to Agents”, 2021) — Roberta Calegari, Giovanni Ciatto, Enrico Denti, Andrea Omicini, Giovanni Sartor

On the Design of PSyKE: A Platform for Symbolic Knowledge Extraction (WOA 2021) — Federico Sabbatini, Giovanni Ciatto, Roberta Calegari, Andrea Omicini

Expectation: Personalized Explainable Artificial Intelligence for Decentralized Agents with Heterogeneous Knowledge (EXTRAAMAS 2021@AAMAS 2021) — Davide Calvaresi, Giovanni Ciatto, Amro Najjar, Reyhan Aydoğan, Leon Van der Torre, Andrea Omicini, Michael I. Schumacher

Towards Explainable Visionary Agents: License to Dare and Imagine (EXTRAAMAS 2021@AAMAS 2021) — Giovanni Ciatto, Amro Najjar, Jean-Paul Calbimonte, Davide Calvaresi

Agent-Based Explanations in AI: Towards an Abstract Framework (EXTRAAMAS 2020@AAMAS 2020) — Giovanni Ciatto, Michael I. Schumacher, Andrea Omicini, Davide Calvaresi

Neuro-symbolic Computation for XAI: Towards a Unified Model (WOA 2020) — Giuseppe Pisano, Giovanni Ciatto, Roberta Calegari, Andrea Omicini

On the integration of symbolic and sub-symbolic techniques for XAI: A survey (Intelligenza Artificiale, 2020) — Roberta Calegari, Giovanni Ciatto, Andrea Omicini

An Abstract Framework for Agent-Based Explanations in AI (AAMAS 2020) — Giovanni Ciatto, Davide Calvaresi, Michael I. Schumacher, Andrea Omicini

Interpretable Narrative Explanation for ML Predictors with LP: A Case Study for XAI (WOA 2019) — Roberta Calegari, Giovanni Ciatto, Jason Dellaluce, Andrea Omicini

Towards XMAS: eXplainability through Multi-Agent Systems (AI&IoT 2019@AIIA 2019) — Giovanni Ciatto, Roberta Calegari, Andrea Omicini, Davide Calvaresi

LPaaS as Micro-intelligence: Enhancing IoT with Symbolic Reasoning (Big Data and Cognitive Computing, 2018) — Roberta Calegari, Giovanni Ciatto, Stefano Mariani, Enrico Denti, Andrea Omicini