Narrative based Postdictive Reasoning for Cognitive Robotics
This work addresses practical integration challenges for cognitive robotics systems in real-world applications, though it appears incremental as it builds on existing action languages and reasoning methods.
The paper tackled the problem of making sense of incomplete and conflicting narrative knowledge for cognitive robotics, specifically in an autonomous wheelchair robot control task, by developing a postdiction-triggered abnormality detection and re-planning system that integrates narrative-based knowledge and answer-set programming for real-time control.
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.