Work

SNN-Based Online Learning of Concepts and Action Laws in an Open World

Year: 2026

Type: article

Abstract: We present the architecture of a fully autonomous, bio-inspired cognitive agent built around a spiking neural network (SNN) implementing the agent’s semantic memory. This agent explores its universe and learns concepts of objects/situations and of its own actions in a one-shot manner. While object/situation concepts are unary, action concepts are triples made up of an initial situation, a motor activity, and an outcome. They embody the agent’s knowledge of its universe’s action laws. Both kinds of concepts have different degrees of generality. To make decisions the agent queries its semantic memory for the expected outcomes of envisaged actions and chooses the action to take on the basis of these predictions. Our experiments show that the agent handles new situations by appealing to previously learned general concepts and rapidly modifies its concepts to adapt to environment changes.

Source: The European Journal on Artificial Intelligence

DOI: 10.1177/30504554251394337

Authors: Christel Grimaud, Dominique Longin, Andreas Herzig

Institutions: Centre National de la Recherche Scientifique, Institut National Polytechnique de Toulouse, Institut de Recherche en Informatique de Toulouse, Université de Toulouse

Language: English

Topic: Psychiatry, Mental Health, Neuroscience

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Agence Nationale de la Recherche

Award: Action, Logical Reasoning and Spiking networks