Graph Neural Networks as the Copula Mundi between Logic and Machine Learning: A Roadmap

by Andrea Agiollo, Giovanni Ciatto, and Andrea Omicini Abstract Combining machine learning (ML) and computational logic (CL) is hard, mostly because of the inherently-different ways they use to represent knowledge. In fact, while ML relies on fixed-size numeric representations leveraging on vectors, matrices, or tensors of real numbers, CL relies on logic terms and clauses—which are unlimited in size and structure. Graph neural networks (GNN) are a novelty in the ML world introduced for dealing with graph-structured data in a sub-symbolic way.
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On the Design of PSyKE: A Platform for Symbolic Knowledge Extraction

by Federico Sabbatini, Giovanni Ciatto, Roberta Calegari, and Andrea Omicini How to access URL: http://ceur-ws.org/Vol-2963/paper14.pdf Abstract 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.
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2P-Kt: A Logic-Based Ecosystem for Symbolic AI

by Giovanni Ciatto, Roberta Calegari, and Andrea Omicini Abstract To date, logic-based technologies are either built on top or as extensions of the Prolog language, mostly working as monolithic solutions tailored upon specific inference procedures, unification mechanisms, or knowledge representation techniques. Instead, to maximise their impact, logic-based technologies should support and enable the general-purpose exploitation of all the manifold contributions from logic programming. Accordingly, we present 2P-Kt, a reboot of the tuProlog project offering a general, extensible, and interoperable ecosystem for logic programming and symbolic AI.
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Towards cooperative argumentation for MAS: an Actor-based approach

by Giuseppe Pisano, Roberta Calegari, and Andrea Omicini Abstract We discuss the problem of cooperative argumentation in multi-agent systems, focusing on the computational model. An actor-based model is proposed as a first step towards cooperative argumentation in multi-agent systems to tackle distribution issues—illustrating a preliminary fully-distributed version of the argumentation process completely based on message passing. How to access URL: http://ceur-ws.org/Vol-2963/paper17.pdf How to cite Bibtex @inproceedings{distributedarg-woa2021, author = {Pisano, Giuseppe and Calegari, Roberta and Omicini, Andrea}, booktitle = {WOA 2021 -- 22nd Workshop ``From Objects to Agents''}, editor = {Calegari, Roberta and Ciatto, Giovanni and Denti, Enrico and Omicini, Andrea and Sartor, Giovanni}, issn = {1613-0073}, keywords = {Argumentation, MAS, cooperative argumentation, distributed argumentation process}, location = {Bologna, Italy}, month = oct, note = {22nd Workshop ``From Objects to Agents'' (WOA 2021), Bologna, Italy, 1--3~} # sep # {~2021.
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On Explainable Negotiations via Argumentation

by Contreras, Victor and Aydoğan, Reyhan and Najjar, Amro and Calvaresi, Davide Abstract TBD How to access URL: http://publications.hevs.ch/index.php/publications/show/2883 How to cite Bibtex @incollection{canc-bnaic-2021-explanable-negotiations, address = {}, author = {Contreras, Victor and Aydoğan, Reyhan and Najjar, Amro and Calvaresi, Davide}, booktitle = {Proceedings of BNAIC 2021}, doi = {}, editor = {}, isbn = {}, isbn-online = {}, issn = {}, keywords = {explainable negotiation}, pages = {}, publisher = {ACM}, series = {}, subseries = {}, title = {On Explainable Negotiations via Argumentation}, url = {http://publications.
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