Abstract. This paper presents a method for learning models of character linguistic style from a corpus of film dialogues and tests the method in a perceptual experiment. We apply our method in the context of SpyFeet, a prototype role playing game. In previous work, we used the PERSONAGE engine to produce restaurant recommendations that varied according to the speaker's personality [14,12]. Here we show for the first time that: (1) our expressive generation engine can operate on content from the story structures of an RPG; (2) PERSONAGE parameter models can be learned from film dialogue; (3) PERSONAGE rule-based models for extraversion and neuroticism are be perceived as intended in a new domain (SpyFeet character utterances); and (4) that the parameter models learned from film dialogue are generally perceived as being similar to the character that the model is based on. This is the first step of our long term goal to create off-theshelf tools to support authors in the creation of interesting dramatic characters and dialogue partners, for a broad range of types of interactive stories and role playing games.

Meaningful choice has often been identified as a key component in a player's engagement with an interactive narrative, but branching stories require tremendous amounts of hand-authored content, in amounts that increase exponentially rather than linearly as more choice points are added. Previous approaches to reducing authorial burden for computer RPGs have relied on creating better tools to manage existing unwieldy structures of quests and dialogue trees. We hypothesize that reducing authorial burden and increasing agency are two sides of the same coin, requiring specific advancements in two related areas of design and technology research: (1) dynamic story management architecture that represents story events abstractly and allows story elements to be selected and re-ordered in response to player choices, and (2) dynamic dialogue generation to allow a single story event to be revealed differently by different characters and in the context of dynamic relationships between those characters and the player. This paper describes SpyFeet, a playable prototype of a storytellingsystem designed to test this hypothesis.

Interactive Narrative often involves dialogue with virtual dramatic characters. In this paper we compare two kinds of models of character style: one based on models derived from the Big Five theory personality, and the other derived from a corpus-based method applied to characters and films from the IMSDb archive. We apply these models to character utterances for a pilot narrative-based outdoor augmented reality game called Murder in the Arboretum. We use an objective quantitative metric to estimate the quality of a character model, with the aim of predicting model quality without perceptual experiments. We show that corpus-based character models derived from individual characters are often more detailed and specific than personality based models, but that there is a strong correlation between personality judgments of original character dialogue and personality judgments of utterances generated for Murder in the Arboretum that use the derived character models.

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