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Abstract: "In recent years, there has been a dramatic proliferation of research concerned with game based learning and intelligent system s . Although the intelligent agent bas ed system has existed for many years, only recently its surge of mainstream popularity motivated researchers to acknowledge its educational value. With the increasing usage of game based learning, reliability requirements for intelligent system s or multi - a gent system s have become more critical. Quiz game is one of the game types which is easy to develop and not restrict ed by any specific subject and any people . And the m ulti - agent based system is flexible and saving resource . Hence we integrate the quiz gam e into multi - agent system s , we called it m ulti - agent based quiz game and also d evelop a framework to build personalized assessment game . For the instructor, they can understand student's playing situation and experience . For the students, they have p ersona lized assessment game . In this paper, we proposed a framewor k of multi - agent based Quiz - MASter , and describe each service from each agent briefly."

"construction of a social cues and training demonstrator (a serious game) that enables its users to improve their social behaviour. In this report, a distinction is made between strong story and strong autonomy approaches to narrative management. The former rely on central management of the narrative through drama managers, not giving their agents much freedom. Inversely, the latter focus on the autonomy of agents, without explicit top-down control over the narrative. The autonomy of such agents allows an unscripted narrative to emerge from the user's interaction with the system. The trade-off between a strict storyline and freedom of action in these approaches is called the narrative paradox. It is concluded that a strong autonomy approach can feature social behaviour of agents more easily than a strong story one, because it is inherent with this approach that its agents have more complex models. For the demonstrator, some control over the narrative is required to let its users reach given goals in the created scenarios. Therefore, our future work will focus on creating a hybrid approach that enables agents to direct the story autonomously."

from the introduction: "Collision avoidance and navigation among virtual agents is an important component of modern video games. Recent de- velopments in commodity hardware, in particular the utiliza- tion of multi-core and many-core architectures in personal computers and consoles are allowing large numbers of vir- tual agents to be incorporated into game levels in increasing numbers and with increasing fidelity. As a result, there is a need for efficient techniques to automatically generate real- istic behaviors for such groups of virtual agents"

Abstract: "Collision avoidance and multi-agent navigation is an important component of modern video games. Recent developments in commodity hardware, in particular the utilization of multi-core and many-core architectures in personal computers and consoles are allowing large numbers of virtual agents to be incorporated into game levels in increasing numbers. We present the hybrid reciprocal velocity obstacle and optimal reciprocal collision avoidance methods for reciprocal collision avoidance and navigation in video games and described their implementations in C++ as HRVO Library and RVO2 Library. The libraries can efficiently simulate groups of twenty-five to one thousand virtual agents in dense conditions and around moving and static obstacles."

"e describe an approach for developing an in- telligent game master (GM) for computer role-playing games. The role of the GM is to set up the game environment, man- age the narrative ow and enforce the game rules whilst keeping the players engaged. Our approach is to use the popular Belief-Desire-Intention (BDI) model of agents to de- veloping a GM."

From the abstract: "Here we demonstrate for the first time the iterative development of agent behaviour for a real-time strategy game (here StarCraft) utilising Behaviour Oriented Design (BOD) [7]. BOD provides focus on the robust creation and easy adjustment of modular and hierarchical cognitive agents. We demonstrate BOD's usage in creating an AI capable of playing the StarCraft character the Zerg hive mind, and document its performance against a variety of opponent AI systems. In describing our tool-driven development process, we also describe the new Abode IDE, provide a brief literature review situating BOD in the AI game literature, and propose possible future work."

Abstract: "This paper introduces Rank-based Interactive Evo- lution (RIE) which is an alternative to interactive evolution driven by computational models of user preferences to generate personalized content. In RIE, the computational models are adapted to the preferences of users which, in turn, are used as fitness functions for the optimization of the generated content. The preference models are built via ranking-based preference learning, while the content is generated via evolutionary search. The proposed method is evaluated on the creation of strategy game maps, and its performance is tested using artificial agents. Results suggest that RIE is both faster and more robust than standard interactive evolution and outperforms other state-of- the-art interactive evolution approaches"

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"

Abstract: "This paper explores the use of modern sensor technologies for physical interaction on educati onal games and interactive spaces. More specifically the thesis studies the potential effect of motion capture and wearable body sensors on educational interactive games, on two aspects: i) on the involvement of human body and motion in the process of lear ning, and recall of knowledge (embodied learning), ii) on assisting the development of basic social emotional competencies, through the enhanced social affordances of embodied games. The paper presents a prototype of an educational game developed using a m otion capture controller and two bio feedback sensors, proposing a generic architecture for multi sensor interactive spaces. Finally the paper provides a review of previously studied modalities for emotion recognition, and examines their application on gam e mechanics and AI game agents."

Abstract: "In our energy-restricted world, planners and engineers have to cope with problems of CO2 emissions and oil- and gas-shortages. Many planning activities not captured under the heading of "futures studies" share common goals like dealing with an uncertain, complex future. We focus on two novel approaches: agent-based modelling and serious games. These approaches, even though they stem from the same general systems theory roots, allow its users to experience "reality" in different ways. This has implications for when and how to use these methods in scanning future developments and how these are communicated."

abstract: "This paper discusses a scenario in which human computation is used to segment timestamped fashion images for mining trends based on visual features of garments (e.g., color and texture) and attributes of portrayed subjects (e.g., gender and age). State-of-the-art algorithms for body part detection and feature extraction can produce low quality results when parts of the body are occluded and when dealing with complex human poses. In such cases, these algorithms could benefit from the assistance of human agents. In order to jointly leverage the potential of crowds and image analysis algorithms, a game with a purpose (GWAP) is proposed, whereby players can help segment images for which specialized algorithms have failed, so as to improve the extraction of color and texture features of garments and their association with the features of the subject wearing them."

As the game models player behavior it can use that data to encourage desired behavior, specifically for wellness apps