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.

Layered Statechart-based AI shows considerable promise by being a highly modular, reusable, and designer friendly approach to game AI. Here we demonstrate the viability of this approach by replicating the functionality of a full-featured and commercial-scale behaviour tree AI within a non-commercial game framework. As well as demonstrating that layered Statecharts are both usable and amply expressive, our experience highlights the value of several, previously unidentified design considerations, such as sensor patterns, the necessity of subsumption, and the utility of orthogonal regions. These observations point towards simplified, higher-level AI construction techniques that can reduce the complexity of AI design and further enhance reuse.

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.

Narrative Intelligence (NI) can help computational systems interact with users, such as through story generation, interactive narratives, and believable virtual characters. However, existing NI techniques generally require manually coded domain knowledge, restricting their scalability. An approach that intelligently, automatically and economically acquires script-like knowledge in any domain with strategic crowdsourcing will ease this bottleneck and broaden the application territory of narrative intelligence. This doctoral consortium paper defines the research problem, describes its significance, proposes a feasible research plan towards a Ph.D. dissertation, and reports on its current progress.

This demonstration presents MaestroGenesis, a program that helps users create complete polyphonic musical pieces from as little as a simple, human composed monophonic melody. MaestroGenesis creates music by exploiting two key ideas behind the functional scaffolding for musical composition (FSMC) approach: (1) that music a function of time and (2) that functional transformations of initial human starting melodies, or scaffolds, inherit some of the essential human qualities contained in the scaffold. Music in FSMC is represented as a functional relationship between the scaffold and a generated accompaniment. The GUI helps users evolve these functions by importing and developing their music through a breeding process akin to animal breeding, called interactive evolutionary computation. Some resulting pieces are indistinguishable from completely human-composed pieces.

The prevalence of virtual worlds presents an interesting The research questions we are looking to solve are: challenge for intelligent mobile agents. Online, very largescale, - How to represent maps of large scale, complex environments persistent virtual worlds such as Second Life (Linden Research Inc. 2012) and massively multi-player online games (MMOs) are becoming more popular. As these - How an agent can generate, update and use these maps worlds grow in size there is a challenge in providing intelligent - How can we utilise user-generated information to build agents that can generate and use maps of these environments and improve upon these maps without the need for hard-coding or pre-processing the map.

Most systems for generating cinematic shot sequences for virtual environments focus on the low-level problems of camera placement. While this approach will create a sequence of camera shots which film individual events in a virtual environment, it does not account for the high-level effects shot sequences have on viewer inferences. There are systems which are based on well known cinematography principles such as the rule of thirds and other framing principals, however these usually utilize schemas or predefined shots and do not reason about the high level cognitive effects on the viewer. In this paper a system is proposed which can reason directly about these high-level cognitive and narrative effects of a shot sequence on the viewer’s mental state.

My research is focused on using human navigation data ingames and simulation to learn motion models from trajectorydata. These motion models can be used to: 1) track the opponent’smovement during periods of network occlusion; 2)learn combat tactics by demonstration; 3) guide the planningprocess when the goal is to intercept the opponent. A trainingset of example motion trajectories is used to learn twotypes of parameterized models: 1) a second order dynamicalsteering model or 2) the reward vector for a Markov DecisionProcess. Candidate paths from the model serve as themotion model in a set of particle filters for predicting the opponent’slocation at different time horizons. Incorporating theproposed motion models into game bots allows them to customizestheir tactics for specific human players and functionas more capable teammates and adversaries.

Xa-lan is a computer program written in Common-LISP to generate music scores with a high level of notational/symbolic expressivity. Generation is driven by audio-analysis of melodic profiles. Once a melodic contour is input to the software, graphic transformations of the original profile stochastically control the different notational elements of the score. The Xa-lan routines display their final output using the ‘Expressive Notation Package’ of PWGL, a LISP-based visual composition environment. A full range of traditional and non-conventional music notation elements can be algorithmically generated with Xa-lan, retrieving to the user a ‘ready-to-play’ or fully ex-pressive music score.

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.