Interactive narrative allows the user to play a role in a story and interact with other characters controlled by the system. Directorial control is a procedure for dynamically tuning the interaction towards the author’s desired effects. Most existing approaches for directorial control are built within plot-centric frameworks for interactive narrative and do not have a systematic way to ensure that the characters are always well-motivated during the interaction. Thespian is a character-centric framework for interactive narrative. In our previous work on Thespian, we presented an approach for applying directorial control while not affecting the consistency of characters ’ motivations. This work evaluates the effectiveness of our directorial control approach. Given the priority of generating only well-motivated characters ’ behaviors, we empirically evaluate how often the author’s desired effects are achieved. We also discuss how the directorial control procedure can save the author effort in configuring the characters.

Abstract Cognitive appraisal theories, which link human emotional experience to their interpretations of events happening in the environment, are leading approaches to model emotions. Cognitive appraisal theories have often been used both for simulating “real emotions” in virtual characters and for predicting the human user’s emotional experience to facilitate human–computer interaction. In this work, we investigate the computational modeling of appraisal in a multi-agent decision-theoretic framework using Partially Observable Markov Decision Process-based (POMDP) agents. Domain-independent approaches are developed for five key appraisal dimensions (motivational relevance, motivation congruence, accountability, control and novelty). We also discuss how the modeling of theory of mind (recursive beliefs about self and others) is realized in the agents and is critical for simulating social emotions. Our model of appraisal is applied to three different scenarios to illustrate its usages. This work not only provides a solution for computationally modeling emotion in POMDPbased agents, but also illustrates the tight relationship between emotion and cognition—the appraisal dimensions are derived from the processes and information required for the agent’s decision-making and belief maintenance processes, which suggests a uniform cognitive structure for emotion and cognition.