Collaborative Planning with Encoding of Users' High-Level Strategies.

TLDR: This work explores a framework in which users provide high-level strategies encoded as soft preferences to guide the low-level search of the planner, and shows that the resulting plans achieve greater similarity to those generated by humans with regard to the produced sequences of actions.

Abstract: The generation of near-optimal plans for multi-agent systems with numerical states and temporal actions is computationally challenging. Current off-the-shelf planners can take a very long time before generating a near-optimal solution. In an effort to reduce plan computation time, increase the quality of the resulting plans, and make them more interpretable by humans, we explore collaborative planning techniques that actively involve human users in plan generation. Specifically, we explore a framework in which users provide high-level strategies encoded as soft preferences to guide the low-level search of the planner. Through human subject experimentation, we empirically demonstrate that this approach results in statistically significant improvements to plan quality, without substantially increasing computation time. We also show that the resulting plans achieve greater similarity to those generated by humans with regard to the produced sequences of actions, as compared to plans that do not incorporate userprovided strategies.