Conventional constraint satisfaction problem (CSP) formulations are static. There is a given set of constraints and variables, and the structure of the constraint graph does not change. For a lot of search problems, though, it is not clear in advance what a solution's constraint graph will look like. To overcome these deficiencies, we introduce the concept of structural constraints, which are restrictions on admissible constraint graphs. The construction of constraint graphs is based on the concept of graph grammars. This allows us to formulate and solve structural constraint satisfaction problems (SCSPs), handling combinatorial search problems without explicitly giving the solution's structure.

Resources such as food, hit points or magical power are standard features of today's computer games. Computer-guided characters often have goals that are related to these resources and must take this into account in planning their behavior. In the field of artificial intelligence, resource-based action planning systems have recently begun to mushroom. However, these systems still tend to neglect resources. Conventional plan length is the primary optimization goal, resources being of only secondary importance. This paper illustrates the problem and gives an overview of the Excalibur agent's planning system, which overcomes these deficiencies by applying an extended constraint programming framework to planning. It allows the search space to be explored without the need to focus on plan length, makes it possible to optimize other criteria like resource-related properties and promotes the inclusion of domain-dependent knowledge to guide and accelerate the search for a plan.

Planning for the real world requires the ability to sense and reason about an arbitrary number of entities and relations that are not known in advance. However, most satisfaction-based planning systems only reason about a fixed number of given unique fluents. This article presents the planning model of the constraint-based Excalibur agent's planning system. The model is based on structural constraint satisfaction, which makes it possible to use arbitrary plan structures and to handle open world planning.

Although domains like continuous workflow planning are promising application candidates for constraint programming techniques, conventional constraint satisfaction representations and algorithms are not expressive and reactive enough. Thus, the present paper extends and combines the concept of structural constraint satisfaction with the use of local search and global constraints to satisfy the advanced requirements.