Task planning and explanation with virtual actions
This addresses planning failures in AI systems, but it appears incremental as it builds on existing graph-based planning methods.
The paper tackles the challenge of identifying and handling planning failures in task planning by introducing virtual actions to ensure a plan is always found, and it was evaluated in three typical scenarios.
One of the challenges of task planning is to find out what causes the planning failure and how to handle the failure intelligently. This paper shows how to achieve this. The idea is inspired by the connected graph: each verticle represents a set of compatible \textit{states}, and each edge represents an \textit{action}. For any given initial states and goals, we construct virtual actions to ensure that we always get a plan via task planning. This paper shows how to introduce virtual action to extend action models to make the graph to be connected: i) explicitly defines static predicate (type, permanent properties, etc) or dynamic predicate (state); ii) constructs a full virtual action or a semi-virtual action for each state; iii) finds the cause of the planning failure through a progressive planning approach. The implementation was evaluated in three typical scenarios.