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

Collaborative Human-Agent Planning for Resilience