Dynamics of a classical conditioning model
Dynamics of a classical conditioning model
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DOI：
10.1023/a:1008965713435
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发表时间：
1999-07-01
影响因子：
3.5
通讯作者：
Morén, J
中科院分区：
文献类型：
--
作者：
Balkenius, C;Morén, J
经典条件反射是动物的一种基本学习机制，几乎可以在所有生物体中找到。如果我们想构建具有与生物学对应物相匹配的能力的机器人，这是需要首先实现的学习机制之一。本文描述了一个经典条件反射的计算模型，其中学习的目标被假设为基于当前刺激情况的时间折扣奖励或惩罚的预测。该模型非常适合机器人实现，因为它模拟了许多经典条件反射范例，并且模型中的学习保证收敛于任意复杂的刺激序列。这是一个基本特征，一旦采取的步骤是超越简单的实验室实验与两个或三个刺激的真实的世界，其中不存在这样的限制。它还展示了该模型如何被包含在一个更复杂的系统中，该系统包括各种形式的感官预处理，以及它如何处理强化学习，响应时间和自适应世界模型。
Classical conditioning is a basic learning mechanism in animals and can be found in almost all organisms. If we want to construct robots with abilities matching those of their biological counterparts, this is one of the learning mechanisms that needs to be implemented first. This article describes a computational model of classical conditioning where the goal of learning is assumed to be the prediction of a temporally discounted reward or punishment based on the current stimulus situation.The model is well suited for robotic implementation as it models a number of classical conditioning paradigms and learning in the model is guaranteed to converge with arbitrarily complex stimulus sequences. This is an essential feature once the step is taken beyond the simple laboratory experiment with two or three stimuli to the real world where no such limitations exist. It is also demonstrated how the model can be included in a more complex system that includes various forms of sensory pre-processing and how it can handle reinforcement learning, timing of responses and function as an adaptive world model.