Measuring the quality of plot is a desirable feature for computational narrative systems.One of the notions of plot quality used in narrative theory is called tellability, which can be derived from certain structural properties, namely the types of events present and the way they are connected.These structures include not only actualized events, but also take into account virtual plans and the affective valencies of events.The present paper introduces Marie-Laure Ryan's tellability principles and suggests to computationally model them using an affective multi-agent simulation system.It discusses how such an approach implies a broader understanding of plot than commonly assumed and analysis several existing narrative systems under these considerations.Furthermore, it introduces a plot-graph formalism that allows the computational representation and analysis of the extended plot understanding.An approach to automatically generating the plot-graph is suggested in the context of the introduced multi-agent simulation system.

My thesis aims at conceptualizing and implementing a computational model of narrative generation that is informed by narratological theory as well as cognitive multi-agent simulation models. It approaches this problem by taking a mimetic stance towards fictional characters and investigates how narrative phenomena related to characters can be computationally recreated from a deep character model grounded in multi agent systems. Based on such a conceptualization of narrative it explores how the generation of plot can be controlled, and how the quality of the resulting plot can be evaluated, in dependence of fictional characters. By that it contributes to research on computational creativity by implementing an evaluative storytelling system, and to narratology by proposing a generative narrative theory based on several post-structuralist descriptive theories.