Biological organisms must learn how to control their own bodies to achieve deliberate locomotion, that is, predict their next body position based on their current position and selected action. Such learning is goal-agnostic with respect to maximizing (minimizing) an environmental reward (penalty) signal. A cognitive map learner (CML) is a collection of three separate yet collaboratively trained artificial neural networks which learn to construct representations for the node states and edge actions of an arbitrary bidirectional graph. In so doing, a CML learns how to traverse the graph nodes; however, the CML does not learn when and why to move from one node state to another. This work created CMLs with node states expressed as high dimensional vectors suitable for hyperdimensional computing (HDC), a form of symbolic machine learning (ML). In so doing, graph knowledge (CML) was segregated from target node selection (HDC), allowing each ML approach to be trained independently. The first approach used HDC to engineer an arbitrary number of hierarchical CMLs, where each graph node state specified target node states for the next lower level CMLs to traverse to. Second, an HDC-based stimulus-response experience model was demonstrated per CML. Because hypervectors may be in superposition with each other, multiple experience models were added together and run in parallel without any retraining. Lastly, a CML-HDC ML unit was modularized: trained with proxy symbols such that arbitrary, application-specific stimulus symbols could be operated upon without retraining either CML or HDC model. These methods provide a template for engineering heterogenous ML systems.

翻译：生物有机体必须学习如何控制自身身体以实现有意的运动，即根据当前位置和所选动作预测下一身体位置。这种学习与最大化（或最小化）环境奖励（或惩罚）信号的目标无关。认知地图学习器（CML）是三个独立但协同训练的人工神经网络的集合，这些网络学习为任意双向图的节点状态和边动作构建表征。通过这种方式，CML学会了如何遍历图节点；然而，CML并未学习何时以及为何从一个节点状态移动到另一个。本研究创建了节点状态以高维向量表示的CML，该向量适用于超维计算（HDC）——一种符号机器学习形式。由此，图知识（CML）与目标节点选择（HDC）得以分离，使得每种机器学习方法可独立训练。第一种方法利用HDC设计了任意数量的分层CML，其中每个图节点状态指定了下一层CML需遍历的目标节点状态。其次，针对每个CML，演示了一种基于HDC的刺激-响应经验模型。由于超向量可相互叠加，多个经验模型可无需重新训练而直接相加并并行运行。最后，将CML-HDC机器学习单元模块化：使用代理符号进行训练，使得无需重新训练CML或HDC模型即可操作任意特定应用的刺激符号。这些方法为设计异构机器学习系统提供了模板。