论文标题
通过组合行为模块来动态处理任务中断
Dynamically handling task disruptions by composing together behavior modules
论文作者
论文摘要
生物神经网络在任务中断的情况下运行，因为它们指导生物实现目标。环境施加的子任务流可以破坏熟悉的刺激反应因果关系。例如，掠夺区域的熟悉路径可能会因捕食者的存在而破坏，因此需要“绕行”该区域。弯路可以是已知的替代路径，必须与原始路径动态组成以完成整体任务。在这个项目中，总体基本路径被独立学习的路径模块以插入，替换和删除修改形式的形式破坏，以使所得的修改后的路径对网络进行新颖。然后，在以零散方式学习的这些路径上测试了网络的性能。总而言之，网络必须即时构成新任务。测试了几种网络架构：时间延迟神经网络（TDNN），长期短期记忆（LSTM），时间卷积网络（TCN）和层次神经网络的形态认知。 LSTM和形态认知在此任务中的表现要好得多。
Biological neural networks operate in the presence of task disruptions as they guide organisms toward goals. A familiar stream of stimulus-response causations can be disrupted by subtask streams imposed by the environment. For example, taking a familiar path to a foraging area might be disrupted by the presence of a predator, necessitating a "detour" to the area. The detour can be a known alternative path that must be dynamically composed with the original path to accomplish the overall task. In this project, overarching base paths are disrupted by independently learned path modules in the form of insertion, substitution, and deletion modifications to the base paths such that the resulting modified paths are novel to the network. The network's performance is then tested on these paths that have been learned in piecemeal fashion. In sum, the network must compose a new task on the fly. Several network architectures are tested: Time delay neural network (TDNN), Long short-term memory (LSTM), Temporal convolutional network (TCN), and Morphognosis, a hierarchical neural network. LSTM and Morphognosis perform significantly better for this task.
