We present a metaphor through which to study games: games as conversation, which casts gameplay as a communicative exchange between player and game. We propose to view aspects of gameplay as speech acts, as defined by Austin and Searle, and we present several examples that illustrate the diverse locutionary, illocutionary, and perlocutionary acts present in the design of digital games. Through our perspective, we are able to cast problems relevant to the interactive entertainment community as discourse problems, where an interactive system must determine what to "say," in order to elicit in the minds of players a specific mental model that will allow them to perform successfully in the game. We conclude with a research agenda that proposes to leverage the artificial intelligence paradigm of discourse planning to tackle the discourse problems of interactive entertainment.

Interactive narratives suffer from the narrative paradox: the tension that exists between providing a coherent narrative experience and allowing a player free reign over what she can manipulate in the environment. Knowing what actions a player in such an environment intends to carry out would help in managing the narrative paradox, since it would allow us to anticipate potential threats to the intended narrative experience and potentially mediate or eliminate them. The process of observing player actions and attempting to come up with an explanation for those actions (i.e. the plan that the player is trying to carry out) is the problem of plan recognition. We adopt the framing of narratives as plans and leverage recent advances that cast plan recognition as planning to develop a symbolic plan recognition system as a proof-of-concept model of a player's reasoning in an interactive narrative environment. In this paper we outline the system architecture, report on performance metrics that demonstrate adequate performance for non-trivial domains, and discuss the implications of treating players as plan recognizers.

Story generators typically adopt a pipelined model of generation wherein fabula structure is decided independently and prior to discourse structure. In this paper, we propose a novel story generator, PlotShot, capable of reasoning over discourse materials during fabula generation such that these materials meaningfully constrain the development of a causally and intentionally coherent story. PlotShot incorporates user-supplied photographs as optional story states through an oversubscription planning paradigm. Further, to leverage existing work on planning-based models of generation, we present a technique to compile the photo story planning problem to classical narrative planning. Our system attempts to maximize quality of an illustrated story by analyzing the affinity between a photo and the action it is meant to depict. An evaluation of generated artifacts shows advantage over heuristic baseline techniques.

If you want to hear an artificial intelligence social bot tell this reporter "screw you," watch the video above. My experience with Microsoft's Zo was unexpected. I thought we'd have an interesting chat about random stuff, but she was pretty quick to insult me. Zo is Microsoft's second attempt at testing a social chatbot online. The first went wrong pretty quickly, according to the University of Utah Assistant Professor of Computer Science and Video Games Rogelio Cardona-Rivera.