Task Modifiers for HTN Planning and Acting
This work addresses the need for flexible planning in dynamic environments for AI agents, but it appears incremental as it builds on existing HTN methods.
The authors tackled the problem of enabling agents to adapt objectives in dynamic environments by proposing task modifiers, an extension to hierarchical task network (HTN) planning that modifies task lists based on state, and evaluated it in two environments, including a non-traditional simulation, showing efficacy in handling exogenous events.
The ability of an agent to change its objectives in response to unexpected events is desirable in dynamic environments. In order to provide this capability to hierarchical task network (HTN) planning, we propose an extension of the paradigm called task modifiers, which are functions that receive a task list and a state and produce a new task list. We focus on a particular type of problems in which planning and execution are interleaved and the ability to handle exogenous events is crucial. To determine the efficacy of this approach, we evaluate the performance of our task modifier implementation in two environments, one of which is a simulation that differs substantially from traditional HTN domains.