The CaveUT system:Immersive entertainment based on a game engine

We describe recent developments in the CaveUT software, which supports immersive virtual reality installations based on the Unreal Tournament game engine. CaveUT implements several high-end VR features such as real-time stereoscopy with head and hand tracking. We demonstrate the use of CaveUT in the SAS Cube™, a PC-based CAVE™-like immersive four-screen display. One of the main advantages of the system is to support fully immersive VR while retaining the game engine’s advanced features for interaction and behavioral (or AI) systems. We illustrate the use of CaveUT on two installations: an artistic VR installation and an immersive interactive storytelling system

Enhancing the Behaviorial Fidelity of Synthetic Entities with Human Behavior Models

Human-behavior models (HBMs) and artificial intelligence systems are called on to fill a wide variety of roles in military simulations. Each of the off the shelf human behavior models available today focuses on a specific area of human cognition and behavior. While this makes these HBMs very effective in specific roles, none are single-handedly capable of supporting the full range of roles necessary in an urban military scenario involving asymmetric opponents and potentially hostile civilians. The research presented here explores the integration of three separate human behavior models to support three different roles for synthetic participants in a single simulated scenario. The Soar architecture, focusing on knowledge-based, goal-directed behavior, supports a fire team of U.S. Army Rangers. PMFServ, focusing on a physiologically/stress constrained model of decision-making based on emotional utility, supports civilians that may become hostile. Finally, AI.Implant, focusing on individual and crowd navigation, supports a small group of opposing militia. Due to the autonomy and wide range of behavior supported by the three human behavior models, the scenario is more flexible and dynamic than many military simulations and commercial computer games

Human Behavior Models for Agents in Simulators and Games: Part II Gamebot Engineering with PMFserv

Many producers and consumers of legacy training simulator and game environments are beginning to envision a new era where psycho-socio-physiologic models could be interoperated to enhance their environments\u27 simulation of human agents. This paper explores whether we could embed our behavior modeling framework (described in the companion paper, Part 1) behind a legacy first person shooter 3D game environment to recreate portions of the Black Hawk Down scenario. Section 1 amplifies the interoperability needs and challenges confronting the field, presents the questions that are examined, and describes the test scenario. Sections 2 and 3 review the software and knowledge engineering methodology, respectively, needed to create the system and populate it with bots. Results (Section 4) and discussion (Section 5) reveal that we were able to generate plausible and adaptive recreations of Somalian crowds, militia, women acting as shields, suicide bombers, and more. Also, there are specific lessons learned about ways to advance the field so that such interoperabilities will become more affordable and widespread

Intelligent Lighting for Game Environments

Lighting design is an important topic of game development. There are many functions that lighting assumes in game environments, including directing attention, establishing good action visibility, evoking emotions, setting atmosphere, and providing depth. Current lighting design techniques rely on static manually designed lighting, where designers set up the positions, angles, and colors for each light in a level. Game environments are dynamic and unpredictable; physical and narrative scene content, including character locations, tension, and narrative goals, change unpredictably in real time due to user interaction. Thus, current static techniques often do not adequately adapt to serve desired aesthetic and communicative functions or perceptual effects. Recently, Doom 3 incorporated dynamic real-time lighting and demonstrated many advantages of using real-time dynamic lighting in games, including heightening the emotional engagement and enhancing the overall interactive experience. However, the technique is scripted and tightly coupled to game content. In this article, we present ELE (Expressive Lighting Engine), an intelligent lighting system that automatically sets and adjusts scene lighting in real time to achieve aesthetic and communicative functions, including evoking emotions, directing visual focus, and providing visibility and depth. ELE operates as a separate system that interacts with game/graphics engines through a standard interface. In this article, we will discuss ELE and its interface with Unreal Tournament 2003. We will also present results showing ELE in action. These results show: the utility of real-time adaptive lighting in providing visual focus, setting atmosphere, evoking emotions, and establishing visibility during interaction in interactive environments; and acceleration in the development process due to the introduction of an automatic system for lighting that can be overridden by designers at a high level, thus eliminating the time-consuming process of setting individual light parameters for each level and scene

Gameplay, Interactive Drama, and Training: Authoring Edutainment Stories for Online Players (AESOP)

This paper describes initial efforts at providing some of the technological advances of the videogame genres in a coherent, accessible format to teams of educators. By providing these capabilities inside an interactive drama generator, we believe that the full potential of educational games may eventually be realized. Sections 1 and 2 postulate three goals for reaching that objective: a toolset for interactive drama authoring, ways to insulate authors from game engines, and reusable digital casts to facilitate composability. Sections 3 and 4 present progress on those tools and an in-depth case study that made use of the resulting toolset to create a large interactive drama. We close with lessons learned to date and a look at the remaining challenges: the unpleasant reality that state-of-the-art tools are not yet able to boost the productivity of edutainment authors

Route Generation for a Synthetic Character (BOT) Using a Partial or Incomplete Knowledge Route Generation Algorithm in UT2004 Virtual Environment

This paper presents a new Route Generation Algorithm that accurately and realistically represents human route planning and navigation for Military Operations in Urban Terrain (MOUT). The accuracy of this algorithm in representing human behavior is measured using the Unreal Tournament(Trademark) 2004 (UT2004) Game Engine to provide the simulation environment in which the differences between the routes taken by the human player and those of a Synthetic Agent (BOT) executing the A-star algorithm and the new Route Generation Algorithm can be compared. The new Route Generation Algorithm computes the BOT route based on partial or incomplete knowledge received from the UT2004 game engine during game play. To allow BOT navigation to occur continuously throughout the game play with incomplete knowledge of the terrain, a spatial network model of the UT2004 MOUT terrain is captured and stored in an Oracle 11 9 Spatial Data Object (SOO). The SOO allows a partial data query to be executed to generate continuous route updates based on the terrain knowledge, and stored dynamic BOT, Player and environmental parameters returned by the query. The partial data query permits the dynamic adjustment of the planned routes by the Route Generation Algorithm based on the current state of the environment during a simulation. The dynamic nature of this algorithm more accurately allows the BOT to mimic the routes taken by the human executing under the same conditions thereby improving the realism of the BOT in a MOUT simulation environment

Using machine learning techniques to create AI controlled players for video games

This study aims to achieve higher replay and entertainment value in a game through human-like AI behaviour in computer controlled characters called bats. In order to achieve that, an artificial intelligence system capable of learning from observation of human player play was developed. The artificial intelligence system makes use of machine learning capabilities to control the state change mechanism of the bot. The implemented system was tested by an audience of gamers and compared against bats controlled by static scripts. The data collected was focused on qualitative aspects of replay and entertainment value of the game and subjected to quantitative analysi

Are the effects of unreal violent videogames pronounced when playing with a virtual reality system?

This study was conducted to analyze the short-term effects of violent electronic games, played with or without a virtual reality (VR) device, on the instigation of aggressive behavior. Physiological arousal (heart rate (HR)), priming of aggressive thoughts, and state hostility were also measured to test their possible mediation on the relationship between playing the violent game (VG) and aggression. The participants—148 undergraduate students—were randomly assigned to four treatment conditions: two groups played a violent computer game (Unreal Tournament), and the other two a non-violent game (Motocross Madness), half with a VR device and the remaining participants on the computer screen. In order to assess the game effects the following instruments were used: a BIOPAC System MP100 to measure HR, an Emotional Stroop task to analyze the priming of aggressive and fear thoughts, a self-report State Hostility Scale to measure hostility, and a competitive reaction-time task to assess aggressive behavior. The main results indicated that the violent computer game had effects on state hostility and aggression. Although no significant mediation effect could be detected, regression analyses showed an indirect effect of state hostility between playing a VG and aggressio