Facing long-term autonomy with a cognitive architecture raises several difficulties for processing symbolic and sub-symbolic information under different levels of uncertainty, and deals with complex decision-making scenarios. For reducing environment uncertainty and simplify the decision-making process, this paper establishes a method for translating robot knowledge to a conceptual graph to later extract probabilistic context information that allows to bound of the actions present at the deliberative layer. This research develops two ROS components, one for translating robot knowledge to the conceptual graphs and one for extracting context knowledge from this graph using Bayesian networks. We evaluate these components in a real-world scenario, performing a task where a robot notifies to a user a message of an event at home. Our results show an improvement in task completion when using our approach, decreasing the planning requests by 65% and doing the task in a third of the time.

面对具有认知架构的长期自治，在不同程度的不确定性下处理符号和亚符号信息带来了一些困难，并处理了复杂的决策场景。为了减少环境不确定性并简化决策过程，本文建立了一种将机器人知识转换为概念图，以随后提取概率上下文信息的方法，该概率上下文信息可以限制在协商层出现的动作。这项研究开发了两个ROS组件，一个用于将机器人知识转换为概念图，另一个用于使用贝叶斯网络从该图提取上下文知识。我们在实际场景中评估这些组件，执行一项任务，其中机器人将家庭事件通知给用户。