The use of procedural content generation to support adaptive games is starting to gain momentum in current research. However, there are still many open issues to tackle, namely the reusability of methodologies. Our re...
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The use of procedural content generation to support adaptive games is starting to gain momentum in current research. However, there are still many open issues to tackle, namely the reusability of methodologies. Our research focuses on reusable and generic methods for linking the procedural generation of 3D game worlds with gameplay, as measured by player modelling techniques. As the interface for that link, we propose the use of gameplay semantics, a knowledge representation technique that allows our case-based generator to match content to player models. We present and discuss the implementation of our proposed method in an existing game, Stunt Playground. Gameplay semantics is created by designers in a generic way and is then used to procedurally generate player-matching Stunt Playground game worlds, both at the design and game stage. Current results show that our approach can automatically create such adaptive game content, thus effectively bridging game world designers, procedural generation and gameplay.
Current game worlds often fall short in providing consistency between the visual representation of the world and the way it feels, behaves, and reacts. This problem partly originates from the goal-oriented and cost-ef...
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Current game worlds often fall short in providing consistency between the visual representation of the world and the way it feels, behaves, and reacts. This problem partly originates from the goal-oriented and cost-effective nature of the game development process, which mostly favors ad hoc solutions for one particular game, rather than investing in concepts like reusability and emergent gameplay. In broader terms, we observe that game worlds miss semantics, and we argue that its deployment has the potential to bring about the consistency missing in their content. Therefore, we present a novel approach aimed at enriching virtual entities in game worlds with information about their roles, how they relate to others, and how they can affect and interact with players, NPCs, and with each other. We discuss several requirements to achieve these goals, and introduce a semantic model to represent game worlds. In order to support and validate this model, we have developed Entika, a framework to facilitate the deployment of semantics during game development, as well as its maintenance during run-time. Furthermore, we briefly discuss several applications that demonstrate the power of this semantic model for game worlds. After careful evaluation of our semantic game world model and framework, we conclude that a semantically rich world representation can substantially assist designers in creating much more consistent game worlds.
This paper presents our approach to video genre recognition which we developed for MediaEval 2011 evaluation. We treat the genre recognition task as a classification problem. We encode visual information in standard w...
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ISBN:
(纸本)9789898565037
This paper presents our approach to video genre recognition which we developed for MediaEval 2011 evaluation. We treat the genre recognition task as a classification problem. We encode visual information in standard way using local features and Bag of Word representation. Audio channel is parameterized in similar way starting from its spectrogram. Further, we exploit available automatic speech transcripts and user generated meta-data for which we compute BOW representations as well. It is reasonable to expect that semantic content of a video is strongly related to its genre, and if this semantic information was available it would make genre recognition simpler and more reliable. To this end, we used annotations for 345 semantic classes from TRECVID 2011 semantic indexing task to train semantic class detectors. Responses of these detectors were then used as features for genre recognition. The paper explains the approach in detail, it shows relative performance of the individual features and their combinations measured on MediaEval 2011 genre recognition dataset, and it sketches possible future research. The results show that, although, meta-data is more informative compared to the content-based features, results are improved by adding content-based information to the meta-data. Despite the fact that the semantic detectors were trained on completely different dataset, using them as feature extractors on the target dataset provides better result than the original low-level audio and video features.
Semantics of 3D models is playing a crucial role in games and simulations. In this paper we propose a framework to specify semantics of large sets of 3D models with minimal human involvement. The framework consists of...
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ISBN:
(纸本)9789898565020
Semantics of 3D models is playing a crucial role in games and simulations. In this paper we propose a framework to specify semantics of large sets of 3D models with minimal human involvement. The framework consists of three modules: classification, segmentation and annotation. We associate a few models with tags representing their classes and classify the other models automatically. Once all models have been classified in different groups, we take a certain number of models as template models in each group, and segment these template models interactively. We then use the segmentation method (and parameters) of the template models to segment the rest of the models of the same group automatically. We annotate the interactively segmented parts and use an attributed adjacency graph to represent them. Automatic annotation of the rest of the models is then performed by subgraph matching. Experiments show that the proposed framework can effectively specify semantics of large sets of 3D models.
This work deals with the application of metaheuristics to the fuel consumption minimization problem of hybrid electric vehicles (HEV) considering exactly specified driving cycles. A genetic algorithm, a downhill-simpl...
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We present an efficient runtime cache to accelerate the display of procedurally displaced and textured implicit surfaces, exploiting spatio-temporal coherence between consecutive frames. We cache evaluations of implic...
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We consider the two-echelon location-routing problem (2E-LRP), a well-known problem in freight distribution arising when establishing a two-level transport system with limited capacities. The problem is a generalizati...
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This paper presents a novel framework for efficiently propagating the stroke-based user edits to the regions with similar colors and locations in high resolution images and videos. Our framework is based on the key ob...
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Architectural elements are the components and details of buildings. Their unique set, combination, design, construction technique form the architectural style of buildings. Building facade classification by architectu...
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Directional estimation is a common problem in many tracking applications. Traditional filters such as the Kalman filter perform poorly in a directional setting because they fail to take the periodic nature of the prob...
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ISBN:
(纸本)9781479902842
Directional estimation is a common problem in many tracking applications. Traditional filters such as the Kalman filter perform poorly in a directional setting because they fail to take the periodic nature of the problem into account. We present a recursive filter for directional data based on the Bingham distribution in two dimensions. The proposed filter can be applied to circular filtering problems with 180 degree symmetry, i.e., rotations by 180 degrees cannot be distinguished. It is easily implemented using standard numerical techniques and is suitable for real-time applications. The presented approach is extensible to quaternions, which allow tracking arbitrary three-dimensional orientations. We evaluate our filter in a challenging scenario and compare it to a traditional Kalman filtering approach.
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