Generating Stable Building Block Structures From Sketches

Stephenson, Matthew; Renz, Jochen; Ge, Xiaoyu; Zhang, Peng

doi:10.1109/tg.2019.2908353

Cited by 6 publications

(3 citation statements)

References 33 publications

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“…Numerous research studies have explored various methods for creating game levels in Angry Birds. Prior investigations have tackled this problem from diverse angles, proposing solutions to intriguing challenges, such as generating stable structures within the physical environment , dynamically adjusting the game levels' difficulty (Stephenson and Renz 2019), creating block structures based on hand-drawn sketches (Stephenson et al 2021), generating deceptive levels to deceive AI agents (Gamage et al 2021a), generating novel scenarios for physics environments (Gamage et al 2023(Gamage et al , 2021b and, most recently, using prompt engineering to create prompts for level generation (Taveekitworachai et al 2023). None of the previous studies has treated the level generation for Angry Birds as a task generation problem that incorporates the physical interactions between the game objects.…”

Section: Physics Based Puzzle Gamesmentioning

confidence: 99%

Physics-Based Task Generation through Causal Sequence of Physical Interactions

Gamage,

Pinto,

Stephenson

et al. 2023

AIIDE

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Performing tasks in a physical environment is a crucial yet challenging problem for AI systems operating in the real world. Physics simulation-based tasks are often employed to facilitate research that addresses this challenge. In this paper, first, we present a systematic approach for defining a physical scenario using a causal sequence of physical interactions between objects. Then, we propose a methodology for generating tasks in a physics-simulating environment using these defined scenarios as inputs. Our approach enables a better understanding of the granular mechanics required for solving physics-based tasks, thereby facilitating accurate evaluation of AI systems' physical reasoning capabilities. We demonstrate our proposed task generation methodology using the physics-based puzzle game Angry Birds and evaluate the generated tasks using a range of metrics, including physical stability, solvability using intended physical interactions, and accidental solvability using unintended solutions. We believe that the tasks generated using our proposed methodology can facilitate a nuanced evaluation of physical reasoning agents, thus paving the way for the development of agents for more sophisticated real-world applications.

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Section: Physics Based Puzzle Gamesmentioning

confidence: 99%

Physics-Based Task Generation through Causal Sequence of Physical Interactions

Gamage,

Pinto,

Stephenson

et al. 2023

AIIDE

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“…Algorithm 1 shows the physical entity extraction process that can be applied to a game instance with multiple physical entities. When formulating the algorithm, we define the term supporter using the support graph similar to how authors have defined it in [19]. If the bottom horizontal edge of an object O i is in contact with the top horizontal edge of another object O j (i.e., O i is resting on top of O j ) then the support graph contains an edge pointing from object O i to O j .…”

Section: A Physical Entity Extraction Modulementioning

confidence: 99%

Deceptive Level Generation for Angry Birds

Gamage¹,

Stephenson²,

Pinto³

et al. 2021

Preprint

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The Angry Birds AI competition has been held over many years to encourage the development of AI agents that can play Angry Birds game levels better than human players. Many different agents with various approaches have been employed over the competition's lifetime to solve this task. Even though the performance of these agents has increased significantly over the past few years, they still show major drawbacks in playing deceptive levels. This is because most of the current agents try to identify the best next shot rather than planning an effective sequence of shots. In order to encourage advancements in such agents, we present an automated methodology to generate deceptive game levels for Angry Birds. Even though there are many existing content generators for Angry Birds, they do not focus on generating deceptive levels. In this paper, we propose a procedure to generate deceptive levels for six deception categories that can fool the state-of-the-art Angry Birds playing AI agents. Our results show that generated deceptive levels exhibit similar characteristics of human-created deceptive levels. Additionally, we define metrics to measure the stability, solvability, and degree of deception of the generated levels.Index Terms-deceptive games, level generation, game playing agents, Angry Birds• Red bird: No special power.

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“…This work differs from our own as it generates a unique tileset for a given image, whereas we are attempting to convert an image into a general tileset representing a shared representation for all levels of a game. Closest to our work, Stephenson et al (Stephenson et al 2019) developed a system to convert sketched images of structures made up of rectangular blocks into analogous structures in Angry Birds. While these structures are important portions of Angry Birds levels, they do not include enemies or the terrain found in complete levels.…”

Section: Vision In Gamesmentioning

confidence: 99%

Image-to-Level: Generation and Repair

Chen

Sydora

Burega

et al. 2020

AIIDE

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Procedural content generation via machine learning (PCGML) has recently gained research attention due to its ability to generate new game content with minimal user input. However, thus far those without machine learning expertise have been largely unable to use PCGML to generate content to fit their needs. This paper proposes the use of images as the input for a PCGML process to generate game levels. Intuitively, a user can submit an image, with the system returning the closest valid Super Mario Bros. game level. Our results indicate that at least for domains like Super Mario Bros. we can recreate a target level with high fidelity.

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Generating Stable Building Block Structures From Sketches

Cited by 6 publications

References 33 publications

Physics-Based Task Generation through Causal Sequence of Physical Interactions

Physics-Based Task Generation through Causal Sequence of Physical Interactions

Deceptive Level Generation for Angry Birds

Image-to-Level: Generation and Repair

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