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1. Mario Level Generation From Mechanics Using Scene Stitching

   https://doi.org/10.1109/CoG47356.2020.9231692  

   Green, Michael Cerny; Mugrai, Luvneesh; Khalifa, Ahmed; Togelius, Julian (August 2020, IEEE Conference on Games)

   null (Ed.)

   Video game tutorials allow players to gain mastery over game skills and mechanics. To hone players’ skills, it is beneficial from practicing in environments that promote individ- ual player skill sets. However, automatically generating environ- ments which are mechanically similar to one-another is a non- trivial problem. This paper presents a level generation method for Super Mario by stitching together pre-generated “scenes” that contain specific mechanics, using mechanic-sequences from agent playthroughs as input specifications. Given a sequence of mechanics, the proposed system uses an FI-2Pop algorithm and a corpus of scenes to perform automated level authoring. The proposed system outputs levels that can be beaten using a similar mechanical sequence to the target mechanic sequence but with a different playthrough experience. We compare the proposed system to a greedy method that selects scenes that maximize the number of matched mechanics. Unlike the greedy approach, the proposed system is able to maximize the number of matched mechanics while reducing emergent mechanics using the stitching process. 

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2. Mech-Elites: Illuminating the Mechanic Space of GVG-AI

   https://doi.org/10.1145/3402942.3402954  

   Charity, Megan; Green, Michael Cerny; Khalifa, Ahmed; Togelius, Julian (September 2020, International Conference on the Foundations of Digital Games)

   null (Ed.)

   This paper introduces a fully automatic method of mechanic illumination for general video game level generation. Using the Constrained MAP-Elites algorithm and the GVG-AI framework, this system generates the simplest tile based levels that contain specific sets of game mechanics and also satisfy playability constraints. We apply this method to illuminate the mechanic space for four different games in GVG-AI: Zelda, Solarfox, Plants, and RealPortals. With this system, we can generate playable levels that contain different combinations of most of the possible mechanics. These levels can later be used to populate game tutorials that teach players how to use the mechanics of the game. 

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3. Towards field-of-view prediction for augmented reality applications on mobile devices

   https://doi.org/10.1145/3386293.3397114  

   Wang, Na; Wang, Haoliang; Petrangeli, Stefano; Swaminathan, Viswanathan; Li, Fei; Chen, Songqing (June 2020, MMSys '20: 11th ACM Multimedia Systems Conference)

   By allowing people to manipulate digital content placed in the real world, Augmented Reality (AR) provides immersive and enriched experiences in a variety of domains. Despite its increasing popularity, providing a seamless AR experience under bandwidth fluctuations is still a challenge, since delivering these experiences at photorealistic quality with minimal latency requires high bandwidth. Streaming approaches have already been proposed to solve this problem, but they require accurate prediction of the Field-Of-View of the user to only stream those regions of scene that are most likely to be watched by the user. To solve this prediction problem, we study in this paper the watching behavior of users exploring different types of AR scenes via mobile devices. To this end, we introduce the ACE Dataset, the first dataset collecting movement data of 50 users exploring 5 different AR scenes. We also propose a four-feature taxonomy for AR scene design, which allows categorizing different types of AR scenes in a methodical way, and supporting further research in this domain. Motivated by the ACE dataset analysis results, we develop a novel user visual attention prediction algorithm that jointly utilizes information of users' historical movements and digital objects positions in the AR scene. The evaluation on the ACE Dataset show the proposed approach outperforms baseline approaches under prediction horizons of variable lengths, and can therefore be beneficial to the AR ecosystem in terms of bandwidth reduction and improved quality of users' experience. 

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4. Strata-NeRF: Neural Radiance Fields for Stratified Scenes

   https://doi.org/10.1109/ICCV51070.2023.01614  

   Dhiman, Ankit; Srinath, R; Rangwani, Harsh; Parihar, Rishubh; Boregowda, Lokesh; Sridhar, Srinath; VenkateshBabu, R (October 2023, ICCV 2023)

   Neural Radiance Field (NeRF) approaches learn the underlying 3D representation of a scene and generate photorealistic novel views with high fidelity. However, most proposed settings concentrate on modelling a single object or a single level of a scene. However, in the real world, we may capture a scene at multiple levels, resulting in a layered capture. For example, tourists usually capture a monument’s exterior structure before capturing the inner structure. Modelling such scenes in 3D with seamless switching between levels can drastically improve immersive experiences. However, most existing techniques struggle in modelling such scenes. We propose Strata-NeRF, a single neural radiance field that implicitly captures a scene with multiple levels. Strata-NeRF achieves this by conditioning the NeRFs on Vector Quantized (VQ) latent representations which allow sudden changes in scene structure. We evaluate the effectiveness of our approach in multi-layered synthetic dataset comprising diverse scenes and then further validate its generalization on the real-world RealEstate 10k dataset. We find that Strata-NeRF effectively captures stratified scenes, minimizes artifacts, and synthesizes high-fidelity views compared to existing approaches. 

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5. Identifying Multiple Levels of Heuristic Reasoning Used in Scientific Model Construction: A Framework Grounded in Imagistic Processing

   https://doi.org/10.1007/978-3-031-94709-4_8  

   Clement, John J (August 2025, Springer Nature Switzerland)

   Ippoliti, E; Sterpetti, F (Ed.)

   This chapter first consolidates a set of important heuristic strategies used for constructing innovative scientific models from three books, including studies in the history of genetics and electromagnetism, and an expert think-aloud study in mechanics. Twenty-four strategies are identified, most of which are field-general. Patterns in their use suggest a partially organized hierarchy of interconnected strategies and substrategies, contrary to the view that heuristics are simply tried in random order. Strategies at four different size and time scale levels are described, including larger Modeling Cycle Phases of model generation, evaluation, and modification, each of which can utilize many smaller Tactical Heuristics as substrategies, e.g., analogy, or testing predictions from the model. These in turn can utilize Grounded Imagistic Processes, such as imagistic mental simulation, an important alternative to deduction for evaluating a model by running it. The framework links higher level, serially organized processes with lower level, imagery-based processes. Its intermediate degree of organization is neither anarchistic, nor fully algorithmic. Possible benefits of organization are narrowing the search space involved and balancing sources of model construction and criticism for productive creativity. Unorganized, spontaneous processes are also discussed, along with their possible benefits. 

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