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"The UT^2 game bot, created by computer scientists Jacob Schru, Igor Karpov and Risto Miikkulainen, won the Humanlike Bot Competition at the IEEE World Congress on Computational Intelligence (WCCI 2012). "The idea of the competition is to evaluate how we can make game bots, non-player characters (NPCs) controlled by AI algorithms, appear as human as possible," explains Miikkulainen, professor of computer science in the College of Natural Sciences. "It is generally recognized that NPCs are relatively weak in most video games: their behavior is predictable and mechanical, and they often make mistakes that human players would be unlikely to make. Players often enjoy playing against other humans, because it provides a more interesting game experience. The goal of the competition is to promote more research in human-like bots, as well as evaluate how well we are currently doing in this area."

Abstract: "Game Artificial Intelligence (Game AI) is a subdiscipline of Artificial Intelligence (AI) and Machine Learning (ML) that explores the ways in which AI and ML can augment player experiences in computer games. Storytelling is an integral part of many modern computer games; within games stories create context, motivate the player, and move the action forward. Interactive Narrative is the use of AI to create and manage stories within games, creating the perception that the player is a character in a dynamically unfolding and responsive story. This paper introduces Game AI and focuses on the open research problems of Interactive Narrative."

"Driven by an initiative of the Adult & Paediatric Diabetes Psychology Service of New Zealand, research has been performed to develop new mechanisms, in the form of computer games, to educate children and teenagers about living with diabetes. Aimed at achieving maximum education effectiveness, the ultimate goal of our research is to develop innovative machine learning algorithms that can be used in games to improve their ability to learn about the changing needs of children and to incorporate this intelligence into the game interface. We also plan to collect and report on the information collected from testing our computer games within a small group of children who have been diagnosed with type I diabetics. "

Her name is Angelina: she runs on a heavy-duty Mac server and she's building some addictive computer games for you. Angelina (a tail-recursive acronym for "A Novel Game-Evolving Labrat I've Named ANGELINA") is a project in evolutionary computing by Michael Cook, a PhD candidate at Imperial College in the UK.

"Abstract-While component reuse is a common concept in software engineering, it does not yet have a strong foothold in Computer Game development, in particular the development of computer-controlled game characters. In this work, we take a modular Statechart-based game AI modelling approach and develop a reuse strategy to enable fast development of new AIs. This is aided through the creation of a standardized interface for Statechart modules in a layered architecture. Reuse is enabled at a high-level through functional groups that encapsulate behaviour. These concepts are solidified with the development of the SkyAI tool. SkyAI enables a developer to build and work with a library of modular components to develop new AIs by composing modules, and then output the resulting product to an existing game. Efficacy is demonstrated by reusing AI components from a tank to quickly make a much different AI for a simple animal."

Abstract: "We propose a powerful new tool for conducting research on computational intelligence and games. 'PyVGDL' is a simple, high-level description language for 2D video games, and the accompanying software library permits parsing and instantly playing those games. The streamlined design of the language is based on defining locations and dynamics for simple building blocks, and the interaction effects when such objects collide, all of which are provided in a rich ontology. It can be used to quickly design games, without needing to deal with control structures, and the concise language is also accessible to generative approaches. We show how the dynamics of many classical games can be generated from a few lines of PyVGDL."

Abstract: "A recent trend within computational intelligence and games research is to investigate how to affect video game players' in-game experience by designing and/or modifying aspects of game content. Analysing the relationship between game content, player behaviour and self-reported affective states constitutes an important step towards understanding game experience and constructing effective game adaptation mechanisms. This papers reports on further refinement of a method to understand this relationship by analysing data collected from players, building models that predict player experience and analysing what features of game and player data predict player affect best. We analyse data from players playing 780 pairs of short game sessions of the platform game Super Mario Bros, investigate the impact of the session size and what part of the level that has the major affect on player experience. Several types of features are explored, including item frequencies and patterns extracted through frequent sequence mining."

Abstract: "Abstract Game developers spend a large portion of their time developing and tweaking the articial intelligence in video games. Problems related to productivity in the development of AI have been solved using various modeling techniques in the eld of AI, language design and easier to use editors. Using a domain specic language to assist in describing AI can increase productivity in this area. In addition to this, game developers can be relieved from irrelevant tasks such as worrying about performance, correctness of the implementation, memory management and optimiza- tion data structures and focus on the high level description of the game play. In this thesis, we focus on real-time video games and we investigate the development of a domain-specic language containing the necessary elements to describe and execute strategies to achieve goals in a real-time video game. We develop a domain-specic language to express strategies for computer controlled actors using techniques commonly found in em- bedded domain-specic languages, and in particular embedded domain- specic languages in Haskell. To demonstrate this language we have developed a prototype of a real-time strategy game that uses strategies implemented using the domain-specic language developed in this thesis"

Abstract: "A number of changes are occurring in the field of computer game development: persistent online games, digital distribution platforms, social and mobile games, and the emer- gence of new business models have pushed game development to put heavier emphasis on the live operation of games. Artificial intelligence has long been an important part of game development practices. The forces of change in the industry present an opportunity for Game AI to have new and profound impact on game development practices. Specifically, Game AI agents should act as "producers" responsible for managing a long-running set of live games, their player communities, and real-world context. We characterize a confluence of four major forces at play in the games industry today, together producing a wealth of data that opens unique research opportunities and challenges for Game AI as producers. We enumerate 12 new research areas spawned by these forces and steps toward how they can be addressed by data-driven Game AI Producers"

"We argue that the development of innovative artificial intelligence (AI) systems plays a crucial role in the exploration of currently unreachable spaces. To aid in exploration, we suggest a practice called AI-based game design, an iterative design process that deeply integrates the affordances of an AI system within the context of game design."

From the abstract: "Traditionally, the tasks associated with game AI revolved around non player character (NPC) behavior at dierent levels of control, varying from navigation and pathnding to decision making. Commercial-standard games developed over the last 15 years and current game productions, however, suggest that the traditional challenges of game AI have been well addressed via the use of sophisticated AI approaches, not necessarily following or inspired by advances in academic practices. The marginal penetration of traditional academic game AI methods in industrial productions has been mainly due to the lack of constructive communication between academia and industry in the early days of academic game AI, and the inability of academic game AI to propose methods that would signicantly advance existing development processes or provide scalable solutions to real world problems. Recently, however, there has been a shift of research focus as the current plethora of AI uses in games is breaking the non-player character AI tradition. A number of those alternative AI uses have already shown a signicant potential for the design of better games. This paper presents four key game AI research areas that are currently reshaping the research roadmap in the game AI eld and evidently put the game AI term under a new perspective. These game AI agship research areas include the computational modeling of player experience, the procedural generation of content, the mining of player data on massive-scale and the alternative AI research foci for enhancing NPC capabilities."