论文标题
基于神经可塑性和发育原理的神经网络建筑会在模拟环境中有效地捕获猎物
A neural net architecture based on principles of neural plasticity and development evolves to effectively catch prey in a simulated environment
论文作者
论文摘要
对于A-Life来说，一个深刻的挑战是建造在深处的行为“生活”的代理人。我们建议使用类似于构建和雕刻动物大脑的过程的过程来构建驱动人造代理的网络的建筑和方法。此外，动作的实例化是动态的：整个网络对感官输入进行实时响应以激活效应子，而不是计算最佳行为的表示并将编码的表示形式发送给效应器控制器。有许多参数，我们在特定的猎物捕捉任务的背景下使用进化算法来选择它们。我们认为该体系结构可能对控制小型自动驾驶机器人或无人机很有用，因为它可以快速响应传感器输入的变化。
A profound challenge for A-Life is to construct agents whose behavior is 'life-like' in a deep way. We propose an architecture and approach to constructing networks driving artificial agents, using processes analogous to the processes that construct and sculpt the brains of animals. Furthermore the instantiation of action is dynamic: the whole network responds in real-time to sensory inputs to activate effectors, rather than computing a representation of the optimal behavior and sending off an encoded representation to effector controllers. There are many parameters and we use an evolutionary algorithm to select them, in the context of a specific prey-capture task. We think this architecture may be useful for controlling small autonomous robots or drones, because it allows for a rapid response to changes in sensor inputs.
