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Daniele P. Radicioni [3]Daniele Radicioni [2]
  1. Dual PECCS: A Cognitive System for Conceptual Representation and Categorization.Antonio Lieto, Daniele Radicioni & Valentina Rho - 2017 - Journal of Experimental and Theoretical Artificial Intelligence 29 (2):433-452.
    In this article we present an advanced version of Dual-PECCS, a cognitively-inspired knowledge representation and reasoning system aimed at extending the capabilities of artificial systems in conceptual categorization tasks. It combines different sorts of common-sense categorization (prototypical and exemplars-based categorization) with standard monotonic categorization procedures. These different types of inferential procedures are reconciled according to the tenets coming from the dual process theory of reasoning. On the other hand, from a representational perspective, the system relies on the hypothesis of conceptual (...)
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  2. From Human to Artificial Cognition and Back: New Perspectives on Cognitively Inspired AI Systems.Antonio Lieto & Daniele Radicioni - 2016 - Cognitive Systems Research 39 (c):1-3.
    We overview the main historical and technological elements characterising the rise, the fall and the recent renaissance of the cognitive approaches to Artificial Intelligence and provide some insights and suggestions about the future directions and challenges that, in our opinion, this discipline needs to face in the next years.
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  3.  5
    Meaning in Context: Ontologically and Linguistically Motivated Representations of Objects and Events.Valerio Basile, Tommaso Caselli & Daniele P. Radicioni - 2019 - Applied Ontology 14 (4):335-341.
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    TULSI: An NLP System for Extracting Legal Modificatory Provisions. [REVIEW]Leonardo Lesmo, Alessandro Mazzei, Monica Palmirani & Daniele P. Radicioni - 2013 - Artificial Intelligence and Law 21 (2):139-172.
    In this work we present the TULSI system (so named after Turin University Legal Semantic Interpreter), a system to produce automatic annotations of normative documents through the extraction of modificatory provisions. TULSI relies on a deep syntactic analysis and a shallow semantic interpreter that are illustrated in detail. We report the results of an experimental evaluation of the system and discuss them, also suggesting future directions for further improvement.
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  5. Proceedings of the 3rd International Workshop on Artificial Intelligence and Cognition, Turin, Italy, September 28-29, 2015.Antonio Lieto, Cristina Battaglino, Daniele P. Radicioni & Manuela Sanguinietti (eds.) - 2015
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