Hebbian Learning of a Behavior Network and its Application to a Collision Avoidance by an Autonomous Agent
Hebbian Learning of a Behavior Network and its Application to a Collision Avoidance by an Autonomous Agent
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行为网络的赫布学习及其在自治代理避免碰撞中的应用
DOI：
10.5687/iscie.15.350
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
I. Nishikawa
中科院分区：
文献类型：
--
作者：
K. Kondo;I. Nishikawa
提出了一种行为网络及其学习规则作为智能体在未知环境中自主运动获取的方法。所提出的方法适用于学习的碰撞避免Khepera的仿真模型。运动被分解为8个行为元素，所提出的行为网络是一个简单的单层网络，它将输入传感器空间中的向量映射到与每个行为元素对应的一个输出单元。根据给定的奖励，通过Hebbian类型学习获得权重，该权重评估网络选择的行为。网络的结构，这是可分解成行为元素，使一个简单的扩展网络的输出单元的增加。仿真结果表明，智能体的学习是成功的，并且通过网络的扩展获得了更多的行为模式。由于网络结构和学习规则的简单性，该方法易于应用于其他运动获取任务，并通过相同模型的到达任务的学习仿真验证了该方法的有效性。
A behavior network and its learning rule are proposed as an autonomous motion acquisition method for an agent in an unknown environment. The proposed method is applied to a learning of the collision avoidance by a simulation model of Khepera. The motion is decomposed into 8 behavior elements, and the proposed behavior network is a simple one-layered network, which maps a vector in an input sensor space to one output unit corresponding to each behavior element. The weights are obtained by Hebbian type learning according to a given reward, which evaluates the behavior chosen by the network. The structure of the network, which is decomposable into the behavior elements, enables a straightforward expansion of the network by the addition of output units. A simulation shows the successful learning of the agent, and more various behavior patterns are obtained by the expansion of the network. The proposed method is easily applied to other motion acquisition tasks owing to the simplicity of the network structure and the learning rule, and an example is demonstrated by a learning simulation of the reaching task by the same model.