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Computer Science > Artificial Intelligence

arXiv:2411.12308 (cs)
[Submitted on 19 Nov 2024]

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

Authors:Christel Grimaud (IRIT-LILaC), Dominique Longin (IRIT-LILaC), Andreas Herzig (IRIT-LILaC)
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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. The 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 actions 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.
Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Robotics (cs.RO)
Cite as: arXiv:2411.12308 [cs.AI]
  (or arXiv:2411.12308v1 [cs.AI] for this version)
  https://doi.org/10.48550/arXiv.2411.12308

Submission history

From: Christel Grimaud [view email] [via CCSD proxy]
[v1] Tue, 19 Nov 2024 07:49:22 UTC (568 KB)
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