  Intelligent agents powered by AI planning assist people in complex scenarios,
such as managing teams of semi-autonomous vehicles. However, AI planning models
may be incomplete, leading to plans that do not adequately meet the stated
objectives, especially in unpredicted situations. Humans, who are apt at
identifying and adapting to unusual situations, may be able to assist planning
agents in these situations by encoding their knowledge into a planner at
run-time. We investigate whether people can collaborate with agents by
providing their knowledge to an agent using linear temporal logic (LTL) at
run-time without changing the agent's domain model. We presented 24
participants with baseline plans for situations in which a planner had
limitations, and asked the participants for workarounds for these limitations.
We encoded these workarounds as LTL constraints. Results show that
participants' constraints improved the expected return of the plans by 10% ($p
< 0.05$) relative to baseline plans, demonstrating that human insight can be
used in collaborative planning for resilience. However, participants used more
declarative than control constraints over time, but declarative constraints
produced plans less similar to the expectation of the participants, which could
lead to potential trust issues.
