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Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations

Evolved living beings can anticipate the consequences of their actions in complex multilevel dynamic situations. This ability relies on abstracting the meaning of an action. The underlying brain mechanisms of such semantic processing of information are poorly understood. Here we show how our novel c...

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Autores principales: Calvo Tapia, Carlos, Villacorta-Atienza, José Antonio, Díez-Hermano, Sergio, Khoruzhko, Maxim, Lobov, Sergey, Potapov, Ivan, Sánchez-Jiménez, Abel, Makarov, Valeri A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7031254/
https://www.ncbi.nlm.nih.gov/pubmed/32116635
http://dx.doi.org/10.3389/fnbot.2020.00004
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author Calvo Tapia, Carlos
Villacorta-Atienza, José Antonio
Díez-Hermano, Sergio
Khoruzhko, Maxim
Lobov, Sergey
Potapov, Ivan
Sánchez-Jiménez, Abel
Makarov, Valeri A.
author_facet Calvo Tapia, Carlos
Villacorta-Atienza, José Antonio
Díez-Hermano, Sergio
Khoruzhko, Maxim
Lobov, Sergey
Potapov, Ivan
Sánchez-Jiménez, Abel
Makarov, Valeri A.
author_sort Calvo Tapia, Carlos
collection PubMed
description Evolved living beings can anticipate the consequences of their actions in complex multilevel dynamic situations. This ability relies on abstracting the meaning of an action. The underlying brain mechanisms of such semantic processing of information are poorly understood. Here we show how our novel concept, known as time compaction, provides a natural way of representing semantic knowledge of actions in time-changing situations. As a testbed, we model a fencing scenario with a subject deciding between attack and defense strategies. The semantic content of each action in terms of lethality, versatility, and imminence is then structured as a spatial (static) map representing a particular fencing (dynamic) situation. The model allows deploying a variety of cognitive strategies in a fast and reliable way. We validate the approach in virtual reality and by using a real humanoid robot.
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spelling pubmed-70312542020-02-28 Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations Calvo Tapia, Carlos Villacorta-Atienza, José Antonio Díez-Hermano, Sergio Khoruzhko, Maxim Lobov, Sergey Potapov, Ivan Sánchez-Jiménez, Abel Makarov, Valeri A. Front Neurorobot Neuroscience Evolved living beings can anticipate the consequences of their actions in complex multilevel dynamic situations. This ability relies on abstracting the meaning of an action. The underlying brain mechanisms of such semantic processing of information are poorly understood. Here we show how our novel concept, known as time compaction, provides a natural way of representing semantic knowledge of actions in time-changing situations. As a testbed, we model a fencing scenario with a subject deciding between attack and defense strategies. The semantic content of each action in terms of lethality, versatility, and imminence is then structured as a spatial (static) map representing a particular fencing (dynamic) situation. The model allows deploying a variety of cognitive strategies in a fast and reliable way. We validate the approach in virtual reality and by using a real humanoid robot. Frontiers Media S.A. 2020-02-13 /pmc/articles/PMC7031254/ /pubmed/32116635 http://dx.doi.org/10.3389/fnbot.2020.00004 Text en Copyright © 2020 Calvo Tapia, Villacorta-Atienza, Díez-Hermano, Khoruzhko, Lobov, Potapov, Sánchez-Jiménez and Makarov. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Calvo Tapia, Carlos
Villacorta-Atienza, José Antonio
Díez-Hermano, Sergio
Khoruzhko, Maxim
Lobov, Sergey
Potapov, Ivan
Sánchez-Jiménez, Abel
Makarov, Valeri A.
Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations
title Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations
title_full Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations
title_fullStr Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations
title_full_unstemmed Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations
title_short Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations
title_sort semantic knowledge representation for strategic interactions in dynamic situations
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7031254/
https://www.ncbi.nlm.nih.gov/pubmed/32116635
http://dx.doi.org/10.3389/fnbot.2020.00004
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