  In this work we present a novel approach to solving concurrent multiagent
planning problems in which several agents act in parallel. Our approach relies
on a compilation from concurrent multiagent planning to classical planning,
allowing us to use an off-the-shelf classical planner to solve the original
multiagent problem. The solution can be directly interpreted as a concurrent
plan that satisfies a given set of concurrency constraints, while avoiding the
exponential blowup associated with concurrent actions. Our planner is the first
to handle action effects that are conditional on what other agents are doing.
Theoretically, we show that the compilation is sound and complete. Empirically,
we show that our compilation can solve challenging multiagent planning problems
that require concurrent actions.

Artificial Intelligence