Characters are vital to large video game worlds as they bring a sense of life to the world. However, background characters are known to rarely exhibit any sign of motivated behavior or emotional state. We want to change this by assigning these characters emotions that can be identified through their non-verbal behavior. We feel the addition of emotion will allow players to feel more connected to the game world and make the game world more believable. This paper presents the results of an experiment to test two ways of conveying emotion: 1) through a character's gait and 2) through a character's interactions with the game world. Results from the experiment suggest that a combination of gait and interactions is the most effective method to convey emotion.

We describe ANGELINA3, a system that can automatically develop games along a defined theme, by selecting appropriate multimedia content from a variety of sources and incorporating it into a game's design. We discuss these capabilities in the context of the FACE model for assessing progress in the building of creative systems, and discuss how ANGELINA3 can be improved through further work.

Non-player character diversity enriches game environments increasing their replay value. We propose a method for obtaining character behavior diversity based on the diversity of plans enacted by characters, and demonstrate this method in a scenario in which characters have multiple choices. Using case-based planning techniques, we reuse plans for varied character behavior, which simulate different personality traits.

Heuristic search has been very successful in abstract game domains such as Chess and Go. In video games, however, adoption has been slow due to the fact that state and move spaces are much larger, real-time constraints are harsher, and constraints on computational resources are tighter. In this paper we present a fast search method — Alpha-Beta search for durative moves— that can defeat commonly used AI scripts in RTS game combat scenarios of up to 8 vs. 8 units running on a single core in under 5ms per search episode. This performance is achieved by using standard search enhancements such as transposition tables and iterative deepening, and novel usage of combat AI scripts for sorting moves and state evaluation via playouts. We also present evidence that commonly used combat scripts are highly exploitable — opening the door for a promising line of research on opponent combat modelling.

Real-time heuristic search algorithms obey a constant limit on planning time per move. Agents using these algorithms can execute each move as it is computed, suggesting a strong potential for application to real-time video-game AI. Recently, a breakthrough in real-time heuristic search performance was achieved through the use of case-based reasoning. In this framework, the agent optimally solves a set of problems and stores their solutions in a case base. Then, given any new problem, it seeks a similar case in the case base and uses its solution as an aid to solve the problem at hand. A number of ad hoc approaches to the case base formation problem have been proposed and empirically shown to perform well. In this paper, we investigate a theoretically driven approach to solving the problem. We mathematically relate properties of a case base to the suboptimality of the solutions it produces and subsequently develop an algorithm that addresses these properties directly. An empirical evaluation shows our new algorithm outperforms the existing state of the art on contemporary video-game pathfinding benchmarks.

Bot detection is an emerging problem in social games that requires different approaches from those used in massively multi-player online games (MMOGs). We focus on mouse selections as a key element of bot detection. We hypothesize that certain interface elements result in predictable differences in mouse selections, which we call spatial game signatures, and that those signatures can be used to model player interactions that are specific to the game mechanics and game interface. We performed a study in which users played a game representative of social games. We collected in-game actions, from which we empirically identified these signatures, and show that these signatures result in a viable approach to bot detection. We make three contributions. First, we introduce the idea of spatial game signatures. Second, we show that the assumption that mouse clicks are normally distributed about the center of buttons is not true for every interface element. Finally, we provide methodologies for using spatial game signatures for bot detection.

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.

Morals are an important part of many stories, and central to why storytelling developed in the first place as a means of communication. They have the potential to provide a framework for developing story structure, which could be utilised by modern storytelling systems. To achieve this we need a general representation for morals. We propose patterns of character emotion as a suitable foundation. In this paper, we categorise Aesop’s fables based on the morals they convey, and use them as a source of emotion data corresponding to those morals. We use inductive logic programming to identify relationships between particular patterns of emotion and the morals of the stories in which they arise.