Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations

Abstract

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.

Static internal representation of dynamic situations reveals time compaction in human cognition

“…These responses are often structured as sequences of actions, constructed considering their semantic content, i.e., the meaning of such actions in the context of the global task, which is a compulsory requirement to chain movements and behaviors into sophisticated strategies. The action representation supported by time compaction allows encapsulating the semantic of each potential response [27] .…”

“…This paradigm allows storing previous experiences in a “library”. Then, such a CIR library can be used for fast navigation [23] and manipulation [27] by recalling the CIRs corresponding to given situations. Based on these features, we hypothesized that time compaction is a human cognitive mechanism mainly involved in survival-like decision-making, as attack or defense strategies during agonistic behaviors (fighting), trajectories generation (chasing and escaping), and coordination of actions among several agents (hunting) .…”

Spatial Memory in a Spiking Neural Network with Robot Embodiment

“…Neuro-robotics provide the means for testing artificial neural networks in a similar context. Earlier, several approaches to controlling mobile robots have been proposed (see, e.g., [ 45 , 49 , 50 , 51 , 52 , 53 ]). Let us now adopt such a strategy and study how a robot can use global spatial memory to learn dangerous zones in the environment.…”

Internal representation of future interactions in rats

“…Therefore, the obtained information could open new venues for creating innovative therapeutic strategies to combat cognitive deterioration. In addition, developing transparent and interpretable artificial navigation systems and robots with animal-like navigation abilities can also be aided by understanding the mechanics underlying spatiotemporal cognition in dynamic environments 35,36 .…”