The authors propose a new computational problem called partially ordered top-quality planning, which allows specifying a subset of actions whose ordering in the plan is important, interpolating between the two extremes of considering all orders important or all orders unimportant.
An iterative planning framework for multi-agent systems with hybrid state spaces enables continual improvement of solutions while efficiently using computational resources.