Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations

• Published in: Frontiers in neurorobotics Vol. 14; p. 4
• Main Authors: Calvo Tapia, Carlos, Villacorta-Atienza, José Antonio, Díez-Hermano, Sergio, Khoruzhko, Maxim, Lobov, Sergey, Potapov, Ivan, Sánchez-Jiménez, Abel, Makarov, Valeri A
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 13.02.2020; Frontiers Media S.A
• Subjects: Cognition & reasoning; Cognitive ability; cognitive maps; Cognitive models; Computer applications; Control theory; dynamical systems; Fencing; Hypotheses; Information processing; Knowledge representation; Lethality; manipulation of objects; Memory; neural networks; Neuroscience; Robots; semantic description; Semantics; Strategic planning; manipulation of objects; dynamical systems; cognitive maps; neural networks; semantic description
• ISSN: 1662-5218; 1662-5218
• DOI: 10.3389/fnbot.2020.00004

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.