This paper advocates the exploration of the full state of recorded real-time strategy (RTS) games, by human or robotic players, to discover how to reason about tactics and strategy. We present a dataset of StarCraft games encompassing the most of the games' state (not only player’s orders). We explain one of the possible usages of this dataset by clustering armies on their compositions. This reduction of armies compositions to mixtures of Gaussian allow for strate- gic reasoning at the level of the components. We evaluated this clustering method by predicting the outcomes of battles based on armies compositions' mixtures components.

We describe a heuristic search technique for multi-agent pursuit-evasion games in partially observable Euclidean space where a team of trackers attempt to minimize their uncertainty about an evasive target. Agents' movement and observation capabilities are restricted by polygonal obstacles, while each agent's knowledge of the other agents is limited to direct observation or periodic updates from team members. Our polynomial-time algorithm is able to generate strategies for games in continuous two-dimensional Euclidean space, an improvement over past algorithms that were only applicable to simple gridworld domains. We demonstrate that our algorithm is tolerant of interruptions in communication between agents, continuing to generate good strategies despite long periods of time where agents are unable to communicate directly. Experiments also show that our technique generates effective strategies quickly, with decision times of less than a second for reasonably sized domains with six or more agents.

Complex games such as RTS games are naturally formalized as Markov games. Given a Markov game, it is often possible to hand-code or learn a set of policies that capture the diversity of possible strategies. It is also often possible to hand-code or learn an abstract simulator of the game that can estimate the outcome of playing two strategies against one another from any state. We consider how to use such policy sets and simulators to make decisions in large Markov games. Prior work has considered the problem using an approach we call minimax policy switching. At each decision epoch, all policy pairs are simulated against each other from the current state, and the minimax policy is chosen and used to select actions until the next decision epoch. While intuitively appealing, we show that this switching policy can have arbitrarily poor worst case performance. In response, we describe a modified algorithm, monotone policy switching, whose worst case performance, under certain conditions, is provably no worse than the minimax fixed policy in the set. We evaluate these switching policies in both a simulated RTS game and the real game Wargus. The results show the effectiveness of policy switching when the simulator is accurate, and also highlight challenges in the face of inaccurate simulations.

We present CLASS Q-L (for: class Q-learning) an application of the Q-learning reinforcement learning algorithm to play complete Wargus games. Wargus is a real-time strategy game where players control armies consisting of units of different classes (e.g., archers, knights). CLASS Q-L uses a single table for each class  of unit so that each unit is controlled and updates its class’ Q-table. This enables rapid learning as in Wargus there are many units of the same class. We present initial results of CLASS Q-L against a variety of opponents.

Real-time strategy (RTS) games are known to be one of the most complex game genres for humans to play, as well as one of the most difficult games for computer AI agents to play well. To tackle the task of applying AI to RTS games, recent techniques have focused on a divide-and-conquer approach, splitting the game into strategic components, and developing separate systems to solve each. This trend gives rise to a new problem: how to tie these systems together into a functional real-time strategy game playing agent. In this paper we discuss the architecture of UAlbertaBot, our entry into the 2011/2012 StarCraft AI competitions, and the techniques used to include heuristic search based AI systems for the intelligent automation of both build order planning and unit control for combat scenarios.

In this paper, we discuss motivations for studying interactive narrative in shared, persistent worlds using the established conventions of quest-based MMORPGs.  We present a framework for categorizing the various techniques used in these games according to the interaction between the world model and the quest model .  Using this framework we generalize recent games to present a more dynamic world model, and investigate extensions to the quest model to support storytelling through adaptive quest narratives.

In this paper, we present the Procedural Game Adaptation (PGA) framework: a designer-controlled way to adapt the Changing the dynamics of a video game (i.e., how the dynamics of a given video game during end-user play. When player's actions affect the game world) is a fundamental tool implemented, this framework produces a deterministic, online of video game design. In Pac-Man, eating a power pill allows adaptation agent (called an experience manager (Riedl the player to temporarily defeat the ghosts that pursue et al. 2011)) that automatically performs two tasks: 1) it and threaten her for the vast majority of the game; in Call gathers information about a game's current player, 2) it of Duty 4, taking the perk called "Deep Impact" allows the uses that information to estimate which of several different player's bullets to pass through certain walls without being changes to the game's dynamics will maximize some playerspecific stopped. The parameters of such changes (e.g., how much value (e.g., fun, sense of influence, etc.). the ghosts slow down while vulnerable) are usually determined by the game's designers long before its release, with

This paper describes a gestural approach to interacting with interactive narrative characters that supports co-creativity. It describes our approach using a Microsoft Kinect to created a short scene with an intelligent avatar and an AI-controlled actor. It describes our preliminary user studies and a recommendation for future evaluation.

Interactive narrative is a form of storytelling that adapts to actions performed by users who assume the roles of story characters. To date, interactive narratives are built by hand. In this paper, we introduce Scheherazade, an intelligent system that automatically creates an interactive narrative about any topic from crowdsourced narratives. Our system leverages the experience and creativity of humans by crowdsourcing a corpus of linear narrative examples. It then constructs an executable plot graph, which is a knowledge structure that defines the legal space of an interactive narrative, by learning the plot events, execution precedence, and event separations. We demonstrate the system can successfully construct an interactive narrative based on noisy human input.

Building complete interactive narrative systems is hard. Building systems that are satisfying for naïve users is especially hard since small deficiencies in component technologies can easily destroy the experience for a user. In this paper I argue that we can ameliorate some of these technical limitations through careful choice of genre and style, and discuss a number of properties of farce that make it a particularly attractive choice. Then I will describe work in progress on Punch and Judy AI Playset, a system that allows users to explore possible narratives in the Punch and Judy story world.