The phenomenal growth of social network games in the last five years has left many game designers, game scholars, and long-time game players wondering how these games so effectively engage their audiences. Without a strong understanding of the sources of appeal of social network games, and how they relate to the appeal of past games and other human activities, it has proven difficult to interpret the phenomenon accurately or build upon its successes. In this paper we propose and employ a particular approach to this challenge, analyzing the motivational game design patterns in the popular 'Ville style of game using the lenses of behavioral economics and behavioral psychology, explaining ways these games engage and retain players. We show how such games employ strategies in central, visible ways that are also present (if perhaps harder to perceive) in games with very different mechanics and audiences. Our conclusions point to lessons for game design, game interpretation, and the design of engaging software of any type.

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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.

Knowledge and its attendant phenomena are central to human storytelling and to the human experience more generally, but we find very few games that revolve around these concerns. This works to preclude a whole class of narrative experiences in games, and it also damages character believability. In this paper, we present an AI framework that supports gameplay with non-player characters who observe and form knowledge about the world, propagate knowledge to other characters, misremember and forget knowledge, and lie. We outline this framework through the lens of a gameplay experience that is intended to showcase it, called Talk of the Town, which we are currently developing. From a review of earlier projects, we find that our system has a novel combination of features found only independently across other systems, and that it is among the first to support character memory fallibility.

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PromWeek places players in a typical high-school, abuzz with excitement over the upcoming prom. Players indirectly sculpt the social landscape by having these hapless highschoolers engage in social exchanges with each other. The results of these social exchanges are many and varied-ranging from mild fluctuations in respect to characters professing their eternal love for one another-and are informed by over 5,000 sociocultural considerations encoded in first order logic. Through massaging the interpersonal relationships and learning the personal intricacies of the characters, the player can solve a series of social puzzles; such as making the class-nerd the Prom King, or bringing peace between feuding jocks and preppies.

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