1,840 research outputs found
Experience-driven procedural content generation (extended abstract)
Procedural content generation is an increasingly
important area of technology within modern human-computer
interaction with direct applications in digital games, the semantic
web, and interface, media and software design. The personalization
of experience via the modeling of the user, coupled with the
appropriate adjustment of the content according to user needs
and preferences are important steps towards effective and meaningful
content generation. This paper introduces a framework for
procedural content generation driven by computational models of
user experience we name Experience-Driven Procedural Content
Generation. While the framework is generic and applicable to
various subareas of human computer interaction, we employ
games as an indicative example of content-intensive software that
enables rich forms of interaction.The research was supported, in part, by the FP7 ICT projects
C2Learn (318480) and iLearnRW (318803).peer-reviewe
Player Modeling
Player modeling is the study of computational models of players in games. This includes the detection, modeling, prediction and expression of human player characteristics which are manifested
through cognitive, affective and behavioral patterns. This chapter introduces a holistic view of player modeling and provides a high level taxonomy and discussion of the key components of a player\u27s model. The discussion focuses on a taxonomy of approaches for constructing a player model, the available types of data for the model\u27s input and a proposed classification for the model\u27s output. The chapter provides also a brief overview of some promising applications and a discussion of the key challenges player modeling is currently facing which are linked to the input, the output and the computational model
Game AI revisited
More than a decade after the early research efforts on the
use of artificial intelligence (AI) in computer games and the
establishment of a new AI domain the term “game AI” needs
to be redefined. Traditionally, the tasks associated with
game AI revolved around non player character (NPC) behavior at different levels of control, varying from navigation
and pathfinding to decision making. Commercial-standard
games developed over the last 15 years and current game
productions, however, suggest that the traditional challenges
of game AI have been well addressed via the use of sophisticated AI approaches, not necessarily following or inspired
by advances in academic practices. The marginal penetration of traditional academic game AI methods in industrial
productions has been mainly due to the lack of constructive communication between academia and industry in the
early days of academic game AI, and the inability of academic game AI to propose methods that would significantly
advance existing development processes or provide scalable
solutions to real world problems. Recently, however, there
has been a shift of research focus as the current plethora
of AI uses in games is breaking the non-player character AI
tradition. A number of those alternative AI uses have already shown a significant potential for the design of better
games.
This paper presents four key game AI research areas that
are currently reshaping the research roadmap in the game
AI field and evidently put the game AI term under a new
perspective. These game AI flagship research areas include
the computational modeling of player experience, the procedural generation of content, the mining of player data on
massive-scale and the alternative AI research foci for enhancing NPC capabilities.peer-reviewe
Designer modeling for personalized game content creation tools
With the growing use of automated content creation
and computer-aided design tools in game development,
there is potential for enhancing the design process
through personalized interactions between the software
and the game developer. This paper proposes designer
modeling for capturing the designer’s preferences, goals
and processes from their interaction with a computer-
aided design tool, and suggests methods and domains
within game development where such a model can be
applied. We describe how designer modeling could be
integrated with current work on automated and mixed-
initiative content creation, and envision future directions which focus on personalizing the processes to a
designer’s particular wishes.peer-reviewe
Integrating knowledge tracing and item response theory: A tale of two frameworks
Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing
Evolving personalized content for Super Mario Bros using grammatical evolution
Adapting game content to a particular player's needs and expertise constitutes an important aspect in game design. Most research in this direction has focused on adapting game difficultyto keep the player engaged in the game. Dynamic difficulty adjustment, however, focuses on one aspect of the gameplay experience by adjusting the content to increase ordecrease perceived challenge. In this paper, we introduce a method for automatic level generation for the platform game Super Mario Bros using grammatical evolution. The grammatical evolution-based level generator is used to generate player-adapted content by employing an adaptation mechanism as a fitness function in grammatical evolution to optimizethe player experience of three emotional states: engagement, frustration and challenge. The fitness functions used are models of player experience constructed in our previous work from crowd-sourced gameplay data collected from over 1500 game sessions.peer-reviewe
prototypical implementations ; working packages in project phase II
In this technical report, we present the concepts and first prototypical
imple- mentations of innovative tools and methods for personalized and
contextualized (multimedia) search, collaborative ontology evolution, ontology
evaluation and cost models, and dynamic access and trends in distributed
(semantic) knowledge. The concepts and prototypes are based on the state of
art analysis and identified requirements in the CSW report IV
A novel Big Data analytics and intelligent technique to predict driver's intent
Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars
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