オープンワールドにおける概念と行動法則のSNNベースオンライン学習
SNN-Based Online Learning of Concepts and Action Laws in an Open World
スパイキングニューラルネットワークを意味記憶として用いた自律エージェントが、物体・状況・行動の概念を一回学習で獲得し、予測に基づいて意思決定しながら環境変化に適応する手法を提案。
著者: Christel Grimaud, Dominique Longin, Andreas Herzig
分類: cs.AI, cs.LG, cs.NE, cs.RO
原文アブストラクト
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.