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

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Organizational Gameplay: The Player as Designer of Character Organizations

Looking at the ways in which players interact with computer games (the gameplays), we perceive predominance of character-centered and/or microcontrolled modes of interaction. Despite being well established, these gameplays tend to structure the games in terms of challenges to be fulfilled on an individual basis, or by thinking collectively but having to microcontrol several characters at the same time. From this observation, the paper presents a complementary gameplay in which the player is urged to face collective challenges by designing character organizations. The basic idea is to make the player structure and control group of characters by defining organizational specifications (i.e., definitions of roles, collective strategies, and social norms). During the game, commanded by the player, artificial agents are then instantiated to play the roles and to follow the strategies and norms as defined in the organizational specification. To turn the idea into practice, the paper proposes an abstract architecture comprising three components or layers. This architecture is materialized in a proof of concept prototype that combines the Minecraft game server, JADE agent platform, and MOISE+ organizational model. Variations and possibilities are discussed and the proposal is compared to related work in the literature

Study and analysis of behaviour decision methods of non-player characters in first-person shooters

Non-player characters (NPCs) have a big importance in video games because if they did not exist, games would feel monotone and without life. With the increase of complexity and realism in video games graphics, the behaviour of NPCs needs to keep up to not break the experience of the player. For that reason, new decision methods for NPCs are studied to handle complex behaviours. First person shooters (FPSs) have a big role on implementing novel ways to define behaviour decision methods of NPCs such as behaviour trees and goal-oriented action planning while other game genres end up using their standards. Some decision methods are better than others depending on what kind of behaviour we want the NPCs to possess, thus, we propose to analyse, discuss, and compare different behaviour decision methods of NPCs and implement some examples to showcase these algorithms.Personagens não jogáveis (NPCs) são um dos tópicos mais importantes dos videojogos, pois é graças a eles que os jogos se tornam mais divertidos e menos repetitivos. Com o aumento do realismo e complexidade dos videojogos, é necessário que o comportamento dos NPCs se torne também mais realista. Para resolver esse problema, vários métodos de decisão para NPC foram criados. Jogos de tiro em primeira pessoa (FPSs) são responsáveis por serem os pioneiros em técnicas tais como behaviour trees e goal oriented action planning que são agora utilizados em vários géneros de videojogos como métodos de decisão de NPC. Alguns métodos de decisão são mais apropriados do que outros, dependendo do tipo de comportamento que pretendamos que o NPC exiba. É proposto neste projeto, analisar, comparar e implementar diferentes métodos de decisão de NPCs

A study on like-attracts-like versus elitist selection criterion for human-like social behavior of memetic mulitagent systems

Memetic multi agent system emerges as an enhanced version of multiagent systems with the implementation of meme-inspired computational agents. It aims to evolve human-like behavior of multiple agents by exploiting the Dawkins' notion of a meme and Universal Darwinism. Previous research has developed a computational framework in which a series of memetic operations have been designed for implementing humanlike agents. This paper will focus on improving the human-like behavior of multiple agents when they are engaged in social interactions. The improvement is mainly on how an agent shall learn from others and adapt its behavior in a complex dynamic environment. In particular, we design a new mechanism that supervises how the agent shall select one of the other agents for the learning purpose. The selection is a trade-off between the elitist and like-attracts-like principles. We demonstrate the desirable interactions of multiple agents in two problem domains

Agent Based Modeling in Computer Graphics and Games

As graphics technology has improved in recent years, more and more importance has been placed on the behavior of virtual characters in applications set in virtual worlds in areas such as games, movies and simulations. The behavior of virtual characters should be believable in order to create the illusion that these virtual worlds are populated with living characters. This has led to the application of agent-based modeling to the control of these virtual characters. There are a number of advantages of using agent-based modeling techniques which include the fact that they remove the requirement for hand controlling all agents in a virtual environment, and allow agents in games to respond to unexpected actions by players

On the definition of non-player character behaviour for real-time simulated virtual environments.

Computer games with complex virtual worlds, which are populated by artificial characters and creatures, are the most visible application of artificial intelligence techniques. In recent years game development has been fuelled by dramatic advances in computer graphics hardware which have led to a rise in the quality of real-time computer graphics and increased realism in computer games. As a result of these developments video games are gaining acceptance and cultural significance as a form of art and popular culture. An important factor for the attainment of realism in games is the artificially intelligent behaviour displayed by the virtual entities that populate the games' virtual worlds. It is our firm belief that to further improve the behaviour of virtual entities, game AI development will have to mirror the advances achieved in game graphics. A major contributing factor for these advancements has been the advent of programmable shaders for real-time graphics, which in turn has been significantly simplified by the introduction of higher level programming languages for the creation of shaders. This has demonstrated that a good system can be vastly improved by the addition of a programming language. This thesis presents a similar (syntactic) approach to the definition of the behaviour of virtual entities in computer games. We introduce the term behaviour definition language (BDL), describing a programming language for the definition of game entity behaviour. We specify the requirements for this type of programming language, which are applied to the development and implementation of several behaviour definition languages, culminating in the design of a new game-genre independent behaviour definition (scripting) language. This extension programming language includes several game AI techniques within a single unified system, allowing the use of different methods of behaviour definition. A subset of the language (itself a BDL) was implemented as a proof of concept of this design, providing a framework for the syntactic definition of the behaviour of virtual entities in computer games

Goal-oriented Behaviour for Intelligent Game Agents

This thesis concerns our innovation in game AI techniques, mainly game agents' modeling, planning and learning. The research topic involves the development of a game design software --- Gameme. Our work mainly focus on the development of the core AI module. In this thesis, after discussing the system design of Gameme, we explain our contributions in two parts: off-line design and real-time processing. In off-line design, we present goal-oriented behaviour design and related modeling methodology for game agents. The goal-oriented design provides not only an intuitive behaviour design methodology for non-professional game designers but also efficient support for real-time behaviour control. In particular, the goal-oriented design can be used in modeling agents in different games. The real-time processing component includes planning and learning mechanisms for game agents. These mechanisms are placed in a layered architecture. Basically, a procedural planning mechanism allows game agents to have the ability of fast reaction to their environment. Then, the creative transfer and adaptive learning mechanism trains game agents to learn from their experience and cooperate in teamwork. Furthermore, the unique emergent learning mechanism can allow game agents to have the ability to analyze different PCs' behaviour patterns and to find the suitable strategy to defeat PCs in real-time. Most of the experiments in this thesis are performed in fighting scenarios. We connected the core AI module with a 3D graphics engine in order to have visual testing results. All test cases show that our goal-oriented behaviour design along with planning and learning mechanisms can provide fast, autonomous, collaborative and adaptive behaviour instructions for game agent in real-time game play