RAGE’s technology focus is on creating and integrating reusable components for Applied Gaming. A growing number of components, all of which accommodate pedagogically oriented functions to support game-based learning. These include learning analytics, emotion appraisal, natural language processing, adaptation methods and many other things.

A crucial achievement has been the successful development of the RAGE component-based architecture, which overcomes many technical compatibility and portability issues across the wide diversity of game engines, programming languages and delivery platforms and systems in use.

BML Realizer

Behaviour Mark-up Language (BML) Realizer

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CEM – Character’s Emotions Manager

CEM enables the creation of emotional profiles for characters.

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EDI – Easy Dialogue Integrator

Authoring tool for incorporating virtual characters – player dialogues

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EDM – Emotional Decison Making

This game component manages how characters make decisions based on their emotional state

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GAS – Gaming Analytics Suite

Integrated pack of game components. Provides data and insights on game usage performance

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MED – Multimodal emotion detection

Complete description of the user’s emotional and affective state.

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PBM Player´s Behaviour Monitor

PBM – Player’s Behaviour Monitor

Contributes to better understand changes in player behavior and to identify specific dynamics of behavior, aiming at better adapting games

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P-CAP – Player Competence Adaptation Pack

Evaluates player’s abilities/ skills allowing game to fit to these profiles

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RER – Real Time Excitement Recognizer

Makes the level of player excitement explicit for better gameplay adaptation, monitoring and personalisation purposes

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P-MAP - Player Motivation Adaptation Pack

P-MAP – Player Motivation Adaptation Pack

P-MAP evaluates a players´ motivational state and adapts the game to such motivational state

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PP – Player Profiler

Determines players´ personality profile and supports game adaptation to this profile

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RCI – Role-Play Character Integrator

Populates your game experience with intelligent socio-emotional characters

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ReaderBench Advanced Natural Language Processing Pack

ReaderBench – Advanced Natural Language Processing Pack

Advanced Multi-Lingual Open-Source games components framework that extracts value from texts using NLP

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Real-time Emotion Detection from Facial Expressions

Real-time Emotion Detection from Facial Expressions

Detect emotions from players’ facial expressions

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S2C2 – Socially Skilled Characters Creator

Improves the social skills of game characters

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SDSM – Shared Data Storage Component

SDSM improves games´ data storage management

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Speech I/O

Ready-made implementation of speech or text inputs

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SSC – Storytelling Scenarios Creator

Authoring tools pack for interactive storytelling scenarios creation and management

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SUGAR – Gamification Engine

Compelling gamification, matchmaking and team formation features at hands reach

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2A – Adaptation and Assessment Component

Enables real-time automatic adaptation (and/or assessment) of game difficulty to player’s expertise level

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