Narrative screenplays follow a standardized format fortheir parts (e.g., stage direction, dialogue, etc.) including short descriptions for what, where, when, and howto film the events in the story (shot headings). We created a grammar based on the syntax of shot headings toextract this and other discourse elements for automatic screenplay annotation. We test our annotator on over a thousand raw screenplays from the IMSDb screenplay corpus and make the output available for narrative intelligence research.

We have developed a cinematic annotation scheme and used that scheme to create a shot-by-shot annotated corpus of similar scenes to aid with research in film understanding. This paper introduces and describes the scheme and the created corpus, discusses some of its merits, and summarizes some basic findings.

Games have used different mechanisms along its history to provide agent behavior: FSMs, utility systems, behavior trees and planning methods. In this paper, we present an architecture that aims at incorporating all these approaches into trees of event handling nodes with behaviours as leaves, using rules for combining actions akin to utility systems. This formalism aims to develop hybrid systems in an easier way.

We present a system for quickly prototyping AI code for modeling social interaction among the simulated residents of a skyscraper. The system is built on top of a commercially released indie game but replaces its character AI with a general-purpose logic programming language. These two features together simplify the more tedious parts of creating prototype AI code from scratch, enabling more effort to be focused on the meatier parts of research.

In this paper we describe a level design editor designed as an interface to allow different AI agents to creatively collaborate on level design problems with human designers. We intend to investigate the comparative impacts of different AI techniques on user experience in this context.

We propose the problem of tutorial generation for games, i.e. to generate tutorials which can teach players to play games, as an AI problem. This problem can be approached in several ways, including generating natural language descriptions of game rules, generating instructive game levels, and generating demonstrations of how to play a game using agents that play in a human-like manner. We further argue that the General Video Game AI framework provides a useful testbed for addressing this problem.

This paper describes the accepted entries to the fifth Playable Experiences track to be held at the AIIDE conference. The Playable Experiences track showcases complete works that make use of artificial intelligence techniques as an integral part of the player experience.

We describe our program of PhD work in which a computer program creates topical poems out of text found on Twitter. These poems are made using a combination of natural language processing and crowdsourcing and are part of a general research plan involving the creation and evaluation of computer-generated poetry, grounded in domain-specific research on human creativity.

Video games presents an immense dissonance between its narrative and interactive sections. These two elements are commonly presented in a format where each occurs at a certain time, but rarely simultaneously. Besides, other elements, such as the background music, are also usually pre-defined, denying for the users the condition to establish new forms of creative expression within the system. This work, therefore, intends to propose new systems and algorithms to fit this paradigm.

Multi-agent path finding (MAPF) is a well-studied problem in artificial intelligence, where one needs to find collision-free paths for agents with given start and goal locations. In video games, agents of different types often form teams. In this paper, we demonstrate the usefulness of MAPFalgorithms from artificial intelligence for moving such non-homogeneous teams in congested video game environments.