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Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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Why, What and How to help each Citizen to Understand Artificial Intelligence?
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In: EISSN: 0933-1875 ; KI - Künstliche Intelligenz ; https://hal.inria.fr/hal-03024034 ; KI - Künstliche Intelligenz, Springer Nature, 2021, pp.1610-1987 (2021)
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Formalizing Problem Solving in Computational Thinking : an Ontology approach
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In: IEEE ICDL 2021 – International Conference on Development and Learning 2021 ; https://hal.inria.fr/hal-03324136 ; IEEE ICDL 2021 – International Conference on Development and Learning 2021, Aug 2021, Beijing, China (2021)
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Ontology as neuronal-space manifold: Towards symbolic and numerical artificial embedding
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In: KRHCAI 2021 Workshop on Knowledge Representation for Hybrid & Compositional AI @ KR2021 ; https://hal.inria.fr/hal-03360307 ; KRHCAI 2021 Workshop on Knowledge Representation for Hybrid & Compositional AI @ KR2021, Nov 2021, Hanoi, Vietnam (2021)
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Ontology as neuronal-space manifold: Towards symbolic and numerical artificial embedding
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In: KRHCAI 2021 Workshop on Knowledge Representation for Hybrid & Compositional AI @ KR2021 ; https://hal.inria.fr/hal-03360307 ; KRHCAI 2021 Workshop on Knowledge Representation for Hybrid & Compositional AI @ KR2021, Nov 2021, Hanoi, Vietnam (2021)
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Abstract:
International audience ; Some human cognitive tasks may involve tightly interleaved logical and numerical computations. On the one hand, ontologies allow us to describe symbolic structured knowledge and perform logical inference, providing a rather natural representation of human reasoning as modeled in cognitive psychology. On the other hand, spiking neural networks are a biologically plausible implementation of processing in brain circuits, yet they process numeric vectors rather than symbolic data. Unifying these symbolic and sub-symbolic approaches is still a wide and open question, and the Semantic Pointer Architecture (SPA) based on the Vector Symbolic Architecture (VSA) provides a way to manipulate symbols embedded as numeric vectors that carry semantic information. In this paper, as a step towards filling the symbolic/numerical gap, we propose to map an ontology onto a SPA-based architecture with a preliminary partial implementation into spiking neural networks. More specifically, we focus on ontology standards used in the semantic web such as Resource Description Framework [Schema] (RDF[S]) and the Web Ontology Language (OWL). We provide a detailed implementation example in the case of specific RDFS entailments based on predicate chaining. To that end, we used the neural simulator Nengo with two associative memories in interaction, the first one storing assertions and the second one storing entailment rules. Reporting interesting formal results, our embedding enjoys intrinsic properties allowing semantic reasoning through distributed numerical computing. This original preliminary work thus combines symbolic and numerical approaches for cognitive modeling, which might be useful to model some complex human tasks such as ill-defined problem-solving, involving neuronal knowledge manipulation.
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Keyword:
[INFO]Computer Science [cs]; [SCCO]Cognitive science; Neural Engineering Framework; Neurosymbolism; Ontology; Resource Description Framework; Semantic Pointer Architecture; Vector Symbolic Architecture
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URL: https://hal.inria.fr/hal-03360307
https://hal.inria.fr/hal-03360307v3/document
https://hal.inria.fr/hal-03360307v3/file/AIDE_KRHCAI_KR_2021.pdf
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Ontology as neuronal-space manifold: Towards symbolic and numerical artificial embedding
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In: KRHCAI 2021 Workshop on Knowledge Representation for Hybrid & Compositional AI @ KR2021 ; https://hal.inria.fr/hal-03360307 ; KRHCAI 2021 Workshop on Knowledge Representation for Hybrid & Compositional AI @ KR2021, Nov 2021, Hanoi, Vietnam (2021)
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Learning to Parse Sentences with Cross-Situational Learning using Different Word Embeddings Towards Robot Grounding ...
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Cognitive Architecture and Software Environment for the Design and Experimentation of Survival Behaviors in Artificial Agents
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In: IJCCI 2018 - 10th International Joint Conference on Computational Intelligence ; https://hal.inria.fr/hal-01931497 ; IJCCI 2018 - 10th International Joint Conference on Computational Intelligence, Sep 2018, Seville, Spain (2018)
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Terres neuves agricoles, terres d'élevage en sursis » : trajectoires actuelles et recomposition des espaces agropastoraux dans le Sud- Ouest nigérien
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In: 5ème Rencontres des études africaines en France ; https://hal.archives-ouvertes.fr/hal-02421705 ; 5ème Rencontres des études africaines en France, Jul 2018, Marseille, France (2018)
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Dynamique des ressources pastorales, perception des changements et stratégies d’adaptation des agropasteurs sahéliens : exemple de la commune de Hombori (Mali).
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In: 3ème conférence internationale AMMA-France ; https://hal.archives-ouvertes.fr/hal-02421770 ; 3ème conférence internationale AMMA-France, Nov 2010, Toulouse, France (2010)
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Spatio-Temporal and Complex-Valued Models based on SOM map applied to Speech Recognition
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In: Twentieth International Joint Conference on Artificial Intelligence - IJCAI'2007 ; https://hal.inria.fr/inria-00118122 ; Twentieth International Joint Conference on Artificial Intelligence - IJCAI'2007, Jan 2007, Hyderabad, India (2007)
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Spatio-temporal biologically inspired models for clean and noisy speech recognition
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In: ISSN: 0925-2312 ; Neurocomputing ; https://hal.inria.fr/inria-00186512 ; Neurocomputing, Elsevier, 2007, 71 (1-3), pp.131--136. ⟨10.1016/j.neucom.2007.08.009⟩ (2007)
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Towards Word Semantics from Multi-modal Acoustico-Motor Integration: Application of the Bijama Model to the Setting of Action-Dependant Phonetic Representations
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In: Biomimetic Neural Learning for Intelligent Robots: Intelligent Systems, Cognitive Robotics, and Neuroscience ; https://hal.inria.fr/inria-00000634 ; Stefan Wermter and Günther Palm and Mark Elshaw. Biomimetic Neural Learning for Intelligent Robots: Intelligent Systems, Cognitive Robotics, and Neuroscience, 3575 (3575), Springer-Verlag, pp.144--161, 2005, Lecture Notes in Artificial Intelligence, 3-540-27440-5. ⟨10.1007/b139051⟩ (2005)
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Multi-criteria self-organization: Example of motor-dependent phonetic representation for a multi-modal robot
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In: Neurobotics Workshop of the 27th german conference on Artificial Intelligence ; https://hal.inria.fr/inria-00099901 ; Neurobotics Workshop of the 27th german conference on Artificial Intelligence, Sep 2004, Ulm, Germany, 12 p (2004)
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