Making sense of incomplete and conflicting narrative knowledge in the
presence of abnormalities, unobservable processes, and other real world
considerations is a challenge and crucial requirement for cognitive robotics
systems. An added challenge, even when suitably specialised action languages
and reasoning systems exist, is practical integration and application within
large-scale robot control frameworks.
In the backdrop of an autonomous wheelchair robot control task, we report on
application-driven work to realise postdiction triggered abnormality detection
and re-planning for real-time robot control: (a) Narrative-based knowledge
about the environment is obtained via a larger smart environment framework; and
(b) abnormalities are postdicted from stable-models of an answer-set program
corresponding to the robot's epistemic model. The overall reasoning is
performed in the context of an approximate epistemic action theory based
planner implemented via a translation to answer-set programming.

在自主轮椅机器人控制任务中，通过应用驱动的工作，利用智能环境框架获取基于叙述式知识的环境信息，通过机器人认识模型的稳定模型后预测异常并进行即时控制，具体使用了基于答案集编程的近似认识动作理论计划器实现了整体推理。