Many computer games of all genres pit the player against a succession of increasingly difficult challenges such as combat with computer-controlled enemies and puzzles. Part of the fun of computer games is to master the skills necessary to complete the game. Challenge tailoring is the problem of matching the difficulty of skill-based events over the course of a game to a specific player's abilities. We present a tensor factorization approach to predicting player performance in skill-based computer games. Our tensor factorization approach is data-driven and can predict changes in players' skill mastery over time, allowing more accurate tailoring of challenges. We demonstrate the efficacy and scalability of tensor factorization models through an empirical study of human players in a simple role-playing combat game. We further find a significant correlation between these performance ratings and player subjective experiences of difficulty and discuss ways our model can be used to optimize player enjoyment.

However, due to the small numbers of goals present in existing systems, goal management Autonomous AI systems should be aware of their own goals is a relatively simple affair. Hanheide et al. (2010) describe and be capable of independently formulating behaviour to a system similar in architecture to our own that manages address them. We would ideally like to provide an agent with just two goals, whereas the one discussed in this paper must a collection of competences that allow it to act in novel situations manage upwards of forty. As the number of goals increases, that may not be predictable at design-time. In particular, the potential for goal conflict grows. This leads to a requirement we are interested in the operation of AI systems in for more sophisticated management processes, such as complex, oversubscribed domains where there may exist a dynamic goal re-prioritisation, allowing agents to alter their variety of ways to address high-level goals by composing behaviour to meet changing operational requirements. In the behaviours to achieve a set of sub-goals taken from a larger oversubscribed problem domains we are interested in, encoding set. Our research focusses how such sub-goals might be chosen all possible operating strategies at design time may (i.e.

Spatiotemporal reasoning is a fundamental contributor to effective problem solving. In an effort to design better problem-solving agents, we examined and evaluated the strategies that humans use to solve Tower Defense puzzles, a complex and popular class of real-time strategy games. A consistent and unexpected finding was that humans frequently treated time and space as equivalent. Players stated temporal goals but solved spatial problems. An analysis of human data and computer simulations showed that re-representing temporal problems as spatial problems was beneficial, but treating the two separately can lead to higher scores. The work presented here holds several possibilities for level designers and others who design and analyze maps and spatial arrangements for domains requiring strategic reasoning.

Search-based procedural content generation methods allow video games to introduce new content continually, thereby engaging the player for a longer time while reducing the burden on developers. However, games so far have not explored the potential economic value of unique evolved artifacts. Building on this insight, this paper presents for the first time a Facebook game called Petalz in which players can share flowers they breed themselves with other players through a global marketplace. In particular, the market in this social game allows players to set the price of their evolved aesthetically-pleasing flowers in virtual currency. Furthermore, the transaction in which one player buys seeds from another creates a new social element that links the players in the transaction. The combination of unique user-generated content and social gaming in Petalz facilitates meaningful collaboration between users, positively influences the dynamics of the game, and opens new possibilities in digital entertainment.

The creative nature of games makes trying new ideas desirable, but these changes are sometimes very risky. We need to find ways to minimize risks while we build innovative experiences. Driver San Francisco did this by using Hinted-execution Behavior Trees; this technique allows developers to modify existing AI behaviors dynamically with very low risk, and was used to adapt Driver’s getaway AI to players’ skills.

We present POMCoP, a system for online planning in collaborative domains that reasons about how its actions will affect its understanding of human intentions, and demonstrate its use in building sidekicks for cooperative games. POMCoP plans in belief space. It explicitly represents its uncertainty about the intentions of its human ally, and plans actions which reveal those intentions or hedge against its uncertainty. This allows POMCoP to reason about the usefulness of incorporating information gathering actions into its plans, such as asking questions, or simply waiting to let humans reveal their intentions. We demonstrate POMCoP by constructing a sidekick for a cooperative pursuit game, and evaluate its effectiveness relative to MDP-based techniques that plan in state space, rather than belief space.

Automated sports commentary is a form of automated narrative. Sports commentary exists to keep the viewer informed and entertained. One way to entertain the viewer is by telling brief stories relevant to the game in progress. We introduce a system called the Sports Commentary Recommendation System (SCoReS) that can automatically suggest stories for commentators to tell during games. Through several user studies, we compared commentary using SCoReS to three other types of commentary and show that SCoReS adds significantly to the broadcast across several enjoyment metrics. We also collected interview data from professional sports commentators who positively evaluated a demonstration of the system. We conclude that SCoReS can be a useful broadcast tool, effective at selecting stories that add to the enjoyment and watchability of sports. SCoReS is a step toward automating sports commentary and, thus, automating narrative.

Game balancing is the fine-tuning phase in which a functioning game is adjusted to be deep, fair, and interesting. Balancing is difficult and time-consuming, as designers must repeatedly tweak parameters, and run lengthy playtests to evaluate the effects of these changes. If designers could receive immediate feedback on their designs, they could explore a vast space of variations, and select only the most promising games for playtesting. Such automated design feedback has been difficult to achieve, as there is no mathematical formulation of game balance that unifies many of its forms. We argue for a formulation in which carefully restricted agents are played against standard agents. We develop this restricted-play balance framework, and evaluate its utility by building a tool capable of calculating measures of balance for a large family of games. By applying this tool to an educational card game, we demonstrate how the framework and tool allow designers to rapidly evaluate and iterate on the balance of their games.

A common approach to interactive narrative involves imbuing the computer with all of the potential story pre-authored story experiences (e.g. as beats, plot points, planning operators, etc.). This has resulted in an accepted paradigm where stories are not created by or with the user; rather, the user is given piecemeal access to the story from the gatekeeper of story knowledge: the computer (e.g. as an AI drama manager). This article describes a formal process that provides for the equal co-creation of story-rich experiences, where neither the user nor computer is in a privileged position in an interactive narrative. It describes a new formal approach that acts as a first step for the real-time co-creation of narrative in games that rely on the negotiated shared mental model between a human actor and an AI improv agent.

Automating parts of game creation benefits both professional and amateur game designers and much previous work has already made progress on this front. In this paper we tackle automating level design. We describe a general graph-based representation for game levels and present a preliminary system that leverages this representation. Our system automatically explores existing levels of a 2D platform game using the rapidly-exploring random tree (RRT) algorithm and constructs a compact graph representation from this exploration. Our system can also modify a graph representation on-the-fly to reflect user-directed changes to the existing level structure. This work constitutes an initial step toward the larger goal of automating level design in a general way.