Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

It’s Better to Enjoy Learning than Playing: Motivational Effects of an Educational Live Action Role-playing Game

Game-based learning is supposed to motivate learners. However, to what degree does motivation driven by interest in playing an instructional game affect learning outcomes compared to motivation driven by interest in the very learning process? This is not known. In this study with a unique design and intervention, young adults (N = 128; a heterogeneous sample) learned how to control an electro-mechanical device in a 40-minute-long learning session integrated into a 2-hour-long educational live action role-playing game (edu-LARP). Edu-LARPs are supposedly engaging games where players take part in team role-playing by physically enacting characters in a fictional universe. In our edu-LARP, players had to understand how the to-be-learned device worked in order to win the game. Departing from typical game-based learning research, learning- and playing-related variables were assessed for each learner separately (i.e., a within-subject design). Affective-motivational factors related to playing (rather than learning) predicted learning outcomes in a positive, but considerably weaker, way compared to learning-related, affective-motivational factors. Developed interest in LARP-like games was primarily related to enjoying the game rather than better learning outcomes; whereas, developed interest in the instructional domain was primarily related to enjoyment of learning and better learning outcomes. Overall, autonomous motivation to play was connected to higher learning outcomes, but this connection was weak. &nbsp

Towards fast prototyping of IVAs behavior: Pogamut 2.

Abstract. We present the platform for IVAs development in the human like environment of the first-person shooter game Unreal Tournament 2004. This environment is extendible and supported by vast community of users. Based on our previous experience the problem of fast verification of models of artificial intelligence or IVAs is in implementation issues. The developer spends most of his time solving technical environment dependent issues and malfunctions, which drives him away from his goals. Therefore our modular platform provides a tool, which helps solving those problems and the developer can spend saved time by solving another AI based issues and model verification. The platform is aimed for research and educational purposes

Factors Associated with Revision Surgery after Internal Fixation of Hip Fractures

Background: Femoral neck fractures are associated with high rates of revision surgery after management with internal fixation. Using data from the Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trial evaluating methods of internal fixation in patients with femoral neck fractures, we investigated associations between baseline and surgical factors and the need for revision surgery to promote healing, relieve pain, treat infection or improve function over 24 months postsurgery. Additionally, we investigated factors associated with (1) hardware removal and (2) implant exchange from cancellous screws (CS) or sliding hip screw (SHS) to total hip arthroplasty, hemiarthroplasty, or another internal fixation device. Methods: We identified 15 potential factors a priori that may be associated with revision surgery, 7 with hardware removal, and 14 with implant exchange. We used multivariable Cox proportional hazards analyses in our investigation. Results: Factors associated with increased risk of revision surgery included: female sex, [hazard ratio (HR) 1.79, 95% confidence interval (CI) 1.25-2.50; P = 0.001], higher body mass index (fo

Action Selection for Virtual Humans in Large Environments

This thesis presents ISMA - a representation of procedural knowledge of virtual humans - and S-GHRP, an algorithm for controlling virtual humans exploiting this representation. ISMA and S-GHRP departs from similar approaches in that they are aimed at virtual humans with complex behaviour acting in large environments. Specifically, ISMA and S-GHRP cope with (1) supplying the simulation in run-time with new components (i.e., objects, actions, places), but without necessity of using any machine learning mechanism at the side of virtual humans, (2) simplifying the simulation at the places out of centre of attention using a smooth level-of detail technique for behaviour and space, (3) supporting transition behaviours. ISMA and S-GHRP are based on the perceptual theory of affordances of J. J. Gibson, and practical reasoning framework of M. E. Bratman. Actually, ISMA and S-GHRP present their implementable refinement. Besides presenting the conceptual description of ISMA and S-GHRP, two particular prototype implementations are described.Tato práce pojednává o řízení virtuálních lidí s komplexním chováním ve velkých virtuálních světech. Představuje původní representaci ISMA, která slouží pro popis toho, co a jak mohou virtuální lidé ve svém prostředí provádět, a algoritmus SGHRP, jenž řeší, jak na základě popisu v ISMA generovat konkrétní akce. Od jiných podobných přístupů se ISMA a S-GHRP liší právě v tom, že jsou zaměřeny na popis velkých světů a řízení virtuálních lidí se složitým chováním. Mezi nejdůležitější vlastnosti representace a algoritmu patří to, že (1) umožňují nahrávat do simulace nové komponenty (akce, objety, místa) jako plug-iny, přičemž virtuální lidé se na ně dokáží okamžitě adaptovat bez potřeby mechanismu strojového učení, (2) umožňují automatické pozvolné zjednodušování simulace v místech, které jsou v daný okamžik mimo střed dění, pomocí tzv. level-of-detail techniky pro chování a prostor, (3) pracují s tzv. přechodným chováním. ISMA a S-GHRP vychází z percepční teorie afordancí J. J. Gibsona a aparátu praktického rozhodování M. E. Bratmana. Vedle vlastního představení ISMA a SGHRP jsou v práci rovněž popsány jejich dvě prototypové implementace.Katedra softwaru a výuky informatikyDepartment of Software and Computer Science EducationFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

Action Selection for Virtual Humans in Large Environments

This thesis presents ISMA - a representation of procedural knowledge of virtual humans - and S-GHRP, an algorithm for controlling virtual humans exploiting this representation. ISMA and S-GHRP departs from similar approaches in that they are aimed at virtual humans with complex behaviour acting in large environments. Specifically, ISMA and S-GHRP cope with (1) supplying the simulation in run-time with new components (i.e., objects, actions, places), but without necessity of using any machine learning mechanism at the side of virtual humans, (2) simplifying the simulation at the places out of centre of attention using a smooth level-of detail technique for behaviour and space, (3) supporting transition behaviours. ISMA and S-GHRP are based on the perceptual theory of affordances of J. J. Gibson, and practical reasoning framework of M. E. Bratman. Actually, ISMA and S-GHRP present their implementable refinement. Besides presenting the conceptual description of ISMA and S-GHRP, two particular prototype implementations are described