RT Journal Article
T1 Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations
A1 Calvo Tapia, Carlos
A1 Villacorta Atienza, José Antonio
A1 Díez Hermano, Sergio
A1 Khoruzkho, Maxim
A1 Lobov, Sergey
A1 Potapov, Ivan
A1 Sánchez Jiménez, Abel
A1 Makarov Slizneva, Valeriy
AB 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.
PB Frontiers
SN 1662-5218
YR 2020
FD 2020-02-13
LK https://hdl.handle.net/20.500.14352/95676
UL https://hdl.handle.net/20.500.14352/95676
LA eng
NO This work was supported by the Russian Science Foundation (project 19-12-00394) and by the Spanish Ministry of Science, Innovation and Universities (grant FIS2017-82900-P).
DS Docta Complutense
RD 22 jul 2024