Deceptive Level Generation for Angry Birds

Gamage, Chathura; Pinto, Vimukthini; Renz, Jochen; Stephenson, Matthew

doi:10.1109/cog52621.2021.9619031

Cited by 6 publications

(5 citation statements)

References 16 publications

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“…The term "deceptive games" was employed to identify an automated method for creating Angry Birds game levels that are deceptive enough to fool modern AI agents. However, these AI agents may contain pitfalls themselves, making them more vulnerable to deception [29].…”

Section: Science Birdsmentioning

confidence: 99%

ChatGPT4PCG Competition: Character-like Level Generation for Science Birds

Taveekitworachai¹,

Abdullah²,

Dewantoro³

et al. 2023

Preprint

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This paper presents the first ChatGPT4PCG Competition at the 2023 IEEE Conference on Games. The objective of this competition is for participants to create effective prompts for ChatGPT-enabling it to generate Science Birds levels with high stability and character-like qualities-fully using their creativity as well as prompt engineering skills. ChatGPT is a conversational agent developed by OpenAI. Science Birds is selected as the competition platform because designing an Angry Birds-like level is not a trivial task due to the in-game gravity; the playability of the levels is determined by their stability. To lower the entry barrier to the competition, we limit the task to the generation of capitalized English alphabetical characters. Here, the quality of the generated levels is determined by their stability and similarity to the given characters. A sample prompt is provided to participants for their reference. An experiment is conducted to determine the effectiveness of its modified versions on level stability and similarity by testing them on several characters. To the best of our knowledge, we believe that ChatGPT4PCG is the first competition of its kind and hope to inspire enthusiasm for prompt engineering in procedural content generation.

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Section: Science Birdsmentioning

confidence: 99%

ChatGPT4PCG Competition: Character-like Level Generation for Science Birds

Taveekitworachai¹,

Abdullah²,

Dewantoro³

et al. 2023

Preprint

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show abstract

“…A task template can be solved by a specific strategic physical rule, and all the templates belonging to the same scenario can be solved by the high-level strategic physical rules discussed above. To guarantee this, in the Phy-Q testbed, we hand-crafted the task templates because existing task generators for Angry Birds 51,52 do not generate tasks according to a strategic physical rule. Also, we ensure that, if an agent understood the strategic physical rule to solve the template, it can solve the template without requiring highly accurate shooting, for example, the template can be solved by shooting at a specific object rather than shooting a specific coordinate.…”

Section: Phy-q Testbed Tasks and Evaluationmentioning

confidence: 99%

Phy-Q as a measure for physical reasoning intelligence

et al. 2023

Self Cite

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Humans are well versed in reasoning about the behaviours of physical objects and choosing actions accordingly to accomplish tasks, while this remains a major challenge for artificial intelligence. To facilitate research addressing this problem, we propose a new testbed that requires an agent to reason about physical scenarios and take an action appropriately. Inspired by the physical knowledge acquired in infancy and the capabilities required for robots to operate in real-world environments, we identify 15 essential physical scenarios. We create a wide variety of distinct task templates, and we ensure that all the task templates within the same scenario can be solved by using one specific strategic physical rule. By having such a design, we evaluate two distinct levels of generalization, namely local generalization and broad generalization. We conduct an extensive evaluation with human players, learning agents with various input types and architectures, and heuristic agents with different strategies. Inspired by how the human intelligence quotient is calculated, we define the physical reasoning quotient (Phy-Q score) that reflects the physical reasoning intelligence of an agent using the physical scenarios we considered. Our evaluation shows that (1) all the agents are far below human performance, and (2) learning agents, even with good local generalization ability, struggle to learn the underlying physical reasoning rules and fail to generalize broadly. We encourage the development of intelligent agents that can reach the human-level Phy-Q score.

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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%

“…Interactions between objects in these environments are complex and even slight variations in these interactions can result in significant changes in the overall outcome. As such, researchers have relied on simulation-based techniques when generating physics-based tasks Gamage et al 2021a).…”

Section: Satisfying Constraints Through Simulationmentioning

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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Deceptive Level Generation for Angry Birds

Cited by 6 publications

References 16 publications

ChatGPT4PCG Competition: Character-like Level Generation for Science Birds

ChatGPT4PCG Competition: Character-like Level Generation for Science Birds

Phy-Q as a measure for physical reasoning intelligence

Physics-Based Task Generation through Causal Sequence of Physical Interactions

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