The Prom is a game where the player manages the social life of a group of high school students and creates the situations from which dramatic, thought provoking or at least funny stories can unfold. The setting of The Prom involves a group of alternative high school kids (e.g. Emos, Goths, Geeks, etc.) and their dramatic lives as they prepare for the upcoming school prom. Through creating friendships, making people become enemies, controlling who gets to be in the "in" crowd and much more, the player can shape the social world of the characters. Each character has a distinct personality represented by interests (e.g. what bands they like), needs (e.g. a character may need to demonstrate a certain degree of dominance over others), traits (e.g. being a particularly jealous person), social networks (e.g. to what degree a characters like, are attracted to or respect one another) and social status (e.g. who is dating who).The social artificial intelligence system Comme il Faut ( CiF ) drives this gameplay experience by simulating per character needs and traits, social statuses, social networks, social history and most importantly to gameplay, the outcomes and effects of social games. CiF is a playable computational model of social interactions designed specifically to allow autonomous characters to play social games. By giving player controls to navigate a social, rather than physical, space, The Prom is being created to demonstrate how CiF and social games can create a practically limitless numbers of possibly compelling stories and gameplay.

The attrition game considered in this study is a graph based strategic game which is a movement-prohibited analogue of small-scale combat situations that arise frequently in popular real-time strategy video games. We present proofs that the attrition game, under a variety of assumptions, is a computationally hard problem in general. We also analyze the 1 vs. n unit case, for which we derive optimal target-orderings that can be computed in polynomial time and used as a core for heuristics for the general case. Finally, we present small problem instances that require randomizing moves — a fact that at first glance seems counter-intuitive.

We use a reactive planning language, ABL (Mateas and Stern 2002), to easily express hierarchical patterns of Creating a good level is a time consuming and iterative geometry that can be incorporated into the level, and also process: designers will typically play a level themselves a monitor and react to designer changes. The geometric number of times before showing it to anyone else, simply relationships between level components are given to a to check that it is playable and meets their expectations constraint solver, Choco (Choco Team 2008), as a set of (Castillo and Novak 2008). Making a change to a small constraints that must be satisfied, thus ensuring that the section of a level, such as moving a single piece of generator will never produce an unplayable level. A geometry, can have a wide impact and require much of the diagram desc rastructure is shown in rest of the level to be modified as well.

In this paper, we report progress on making synthetic characters more taskable. In particular, we present an English-like command language that lets one specify complex behaviors an agent should carry out in a virtual environment. We also report compilers that translate English commands into a formal notation and formal statements into procedures for Icarus, an agent architecture that supports reactive execution. To demonstrate the benefits of such taskability, we have integrated Icarus with Twig, which provides a simulated physical environment with humanoid agents. We use the command language to specify three complex activities, including responding to an object contingently, collecting and storing a set of objects, and negotiating with another agent in order to purchase an item. We also discuss related work on controlling synthetic characters, along with paths for additional research on taskability.

In 2010, AIIDE will host the StarCraft AI competition. StarCraft games, simulating the environment faced This event enables researchers to evaluate their by professional gamers. In each tournament, bots are paired up in oneon-one Participants are given the task of building the best matches with the objective of destroying all performing bot for StarCraft: Brood War, which was enemy forces. All the tournaments utilize a best of released by Blizzard Entertainment in 1998. The rule set is goals of the competition are to provide a testbed for based on the ORTS 2009 competition, but relaxes AI and to promote game AI research by exhibiting the rules to enable the development of bots in a variety techniques in an immensely popular game.

A key challenge in computational narrative is story generation. In this paper we focus on analogy-based story generation, and, specifically, on how to generate both story and text using analogy. We present a dual representation formalism where a human-understandable representation (composed of English sentences) and a computer-understandable representation (consisting in a graph) are linked together in order to generate both story and natural language text by analogy. We have implemented our technique in the Riu interactive narrative system.

From subtle political intrigue to outright physical combat, conflict is essential to interesting stories. Narratology research emphasizes that conflict provides structure and engagement, so narrative systems stand to benefit greatly from a computational model of this phenomenon. We present such a model based on AI planing, along with formulas for measuring seven essential properties: participants, subject, duration, directness, intensity, balance, and resolution. We also sketch an algorithm which uses this model to create stories structured around a central struggle.

Learning effective policies in multi-agent adversarial games is a significant challenge since the search space can be prohibitively large when the actions of all the agents are considered simultaneously. Recent advances in Monte Carlo search methods have produced good results in single-agent games like Go with very large search spaces. In this paper, we propose a variation on the Monte Carlo method, UCT (Upper Confidence Bound Trees), for multi-agent, continuous-valued, adversarial games and demonstrate its utility at generating American football plays for Rush Football 2008. In football, like in many other multi-agent games, the actions of all of the agents are not equally crucial to gameplay success. By automatically identifying key players from historical game play, we can focus the UCT search on player groupings that have the largest impact on yardage gains in a particular formation.

Massively Multiplayer Online Games (MMOGs), in their aspect as online communities, represent an exciting opportunity for studying social and behavioral models.  For that purpose we have developed Cosmopolis, a free MMOG containing several key research-oriented features.  First, Cosmopolis consists of an outer game for larger-scale social modeling, as well as a set of subgames suitable for tightly-controlled sandbox-style experiments, all allowing a high level of data logging configuration and control by researchers.  Also, Cosmopolis’s world model incorporates configurable, AI-driven non-player character communities, as a means of researching interactions between individuals and societies

Story Canvas is a visual authoring tool for the creation of interactive, generative stories. Aimed at authors without a technical background in computational storytelling, our system takes an existing author goal-based narrative planning architecture and adds a highly visual authoring and reading interface to the technology, using the language of storyboards and comics as a framework for both authoring and interacting with the resulting narratives. In this paper we describe Story Canvas and its evolution from our previous authoring work, including how our interface choices have been driven by our previous experiences with non-technical authors, and describe the details of translating the visual authoring constructs into story plans within the story generator.