In this article, we describe how real world planning problems can be solved by employing Artificial Intelligence planning techniques. We introduce the paradigm of hybrid planning, which is particularly suited for applications where plans are not intended to be automatically executed by systems, but are made for humans. Hybrid planning combines hierarchical planning – the stepwise refinement of complex tasks – with explicit reasoning about causal dependencies between actions, thereby reflecting exactly the kinds of reasoning humans perform when developing plans. We show how plans are generated and how failed plans are repaired in a way that guarantees stability. Our illustrating examples are taken from a domain model for disaster relief missions enforced upon extensive floods. Finally, we present a tool to support the challenging task of constructing planning domain models.
The article ends with an overview of a wide varity of actual planning applications and outlines further such in the area of cognitive technical systems.