15,215 research outputs found
A spatially-structured PCG method for content diversity in a Physics-based simulation game
This paper presents a spatially-structured evolutionary algorithm (EA) to procedurally generate game maps of di ferent levels of di ficulty to be solved, in Gravityvolve!, a physics-based simulation videogame that we have implemented and which is inspired by the n-
body problem, a classical problem in the fi eld of physics and mathematics. The proposal consists of a steady-state EA whose population is partitioned into three groups according to the di ficulty of the generated content (hard, medium or easy) which can be easily adapted to handle the automatic creation of content of diverse nature in other games. In addition, we present three fitness functions, based on multiple criteria (i.e:, intersections, gravitational acceleration and simulations), that were used experimentally to conduct the search process for creating a database of
maps with di ferent di ficulty in Gravityvolve!.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
Generation and Analysis of Content for Physics-Based Video Games
The development of artificial intelligence (AI) techniques that can assist with the creation and analysis of digital content is a broad and challenging task for researchers. This topic has been most prevalent in the field of game AI research, where games are used as a testbed for solving more complex real-world problems. One of the major issues with prior AI-assisted content creation methods for games has been a lack of direct comparability to real-world environments, particularly those with realistic physical properties to consider. Creating content for such environments typically requires physics-based reasoning, which imposes many additional complications and restrictions that must be considered. Addressing and developing methods that can deal with these physical constraints, even if they are only within simulated game environments, is an important and challenging task for AI techniques that intend to be used in real-world situations.
The research presented in this thesis describes several approaches to creating and analysing levels for the physics-based puzzle game Angry Birds, which features a realistic 2D environment. This research was multidisciplinary in nature and covers a wide variety of different AI fields, leading to this thesis being presented as a compilation of published work. The central part of this thesis consists of procedurally generating levels for physics-based games similar to those in Angry Birds. This predominantly involves creating and placing stable structures made up of many smaller blocks, as well as other level elements. Multiple approaches are presented, including both fully autonomous and human-AI collaborative methodologies. In addition, several analyses of Angry Birds levels were carried out using current state-of-the-art agents. A hyper-agent was developed that uses machine learning to estimate the performance of each agent in a portfolio for an unknown level, allowing it to select the one most likely to succeed. Agent performance on levels that contain deceptive or creative properties was also investigated, allowing determination of the current strengths and weaknesses of different AI techniques. The observed variability in performance across levels for different AI techniques led to the development of an adaptive level generation system, allowing for the dynamic creation of increasingly challenging levels over time based on agent performance analysis. An additional study also investigated the theoretical complexity of Angry Birds levels from a computational perspective.
While this research is predominately applied to video games with physics-based simulated environments, the challenges and problems solved by the proposed methods also have significant real-world potential and applications
Utilizing Generative Adversarial Networks for Stable Structure Generation in Angry Birds
This paper investigates the suitability of using Generative Adversarial
Networks (GANs) to generate stable structures for the physics-based puzzle game
Angry Birds. While previous applications of GANs for level generation have been
mostly limited to tile-based representations, this paper explores their
suitability for creating stable structures made from multiple smaller blocks.
This includes a detailed encoding/decoding process for converting between Angry
Birds level descriptions and a suitable grid-based representation, as well as
utilizing state-of-the-art GAN architectures and training methods to produce
new structure designs. Our results show that GANs can be successfully applied
to generate a varied range of complex and stable Angry Birds structures.Comment: 11 pages, 10 figures, 2 tables, Accepted at the 19th AAAI Conference
on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 23
Physics-Based Task Generation through Causal Sequence of Physical Interactions
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.Comment: The 19th AAAI Conference on Artificial Intelligence and Interactive
Digital Entertainment (AIIDE-23
AI Researchers, Video Games Are Your Friends!
If you are an artificial intelligence researcher, you should look to video
games as ideal testbeds for the work you do. If you are a video game developer,
you should look to AI for the technology that makes completely new types of
games possible. This chapter lays out the case for both of these propositions.
It asks the question "what can video games do for AI", and discusses how in
particular general video game playing is the ideal testbed for artificial
general intelligence research. It then asks the question "what can AI do for
video games", and lays out a vision for what video games might look like if we
had significantly more advanced AI at our disposal. The chapter is based on my
keynote at IJCCI 2015, and is written in an attempt to be accessible to a broad
audience.Comment: in Studies in Computational Intelligence Studies in Computational
Intelligence, Volume 669 2017. Springe
ChatGPT4PCG Competition: Character-like Level Generation for Science Birds
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.Comment: This paper under review is made available for participants of
ChatGPT4PCG Competition (https://chatgpt4pcg.github.io/) and readers
interested in relevant area
The Difficulty of Novelty Detection in Open-World Physical Domains: An Application to Angry Birds
Detecting and responding to novel situations in open-world environments is a
key capability of human cognition. Current artificial intelligence (AI)
researchers strive to develop systems that can perform in open-world
environments. Novelty detection is an important ability of such AI systems. In
an open-world, novelties appear in various forms and the difficulty to detect
them varies. Therefore, to accurately evaluate the detection capability of AI
systems, it is necessary to investigate the difficulty to detect novelties. In
this paper, we propose a qualitative physics-based method to quantify the
difficulty of novelty detection focusing on open-world physical domains. We
apply our method in a popular physics simulation game, Angry Birds. We conduct
an experiment with human players with different novelties in Angry Birds to
validate our method. Results indicate that the calculated difficulty values are
in line with the detection difficulty of the human players
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