AI planning has featured in a number of Interactive story-telling prototypes: since narratives can be naturally modelled as a sequence of actions it has been possible to exploit state of the art planners in the task of narrative generation. However the characteristics of a "good" plan, such as optimality, aren't necessarily the same as those of a "good" narrative, where errors and convoluted sequences may offer more reader interest, so some narrative structuring is required. In their work the authors have looked at injecting narrative control into plan generation through the use of PDDL3.0 state trajectory constraints which enable them to express narrative control information within the planning representation. As part of this the authors have developed an approach to planning with such trajectory constraints. The approach decomposes the problem into a set of smaller subproblems using the temporal orderings described by the constraints and then solves these subproblems incrementally. In this paper the authors outline their method and present results that illustrate the potential of the approach.
|Title of host publication|| Lecture Notes in Computer Science|
|Editors||Ida A Lurgel, Nelson Zagalo, Paolo Petta|
|Place of Publication||Heidelberg|
|Publication status||Published - 2009|