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

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

RaidEnv: Exploring New Challenges in Automated Content Balancing for Boss Raid Games

The balance of game content significantly impacts the gaming experience. Unbalanced game content diminishes engagement or increases frustration because of repetitive failure. Although game designers intend to adjust the difficulty of game content, this is a repetitive, labor-intensive, and challenging process, especially for commercial-level games with extensive content. To address this issue, the game research community has explored automated game balancing using artificial intelligence (AI) techniques. However, previous studies have focused on limited game content and did not consider the importance of the generalization ability of playtesting agents when encountering content changes. In this study, we propose RaidEnv, a new game simulator that includes diverse and customizable content for the boss raid scenario in MMORPG games. Additionally, we design two benchmarks for the boss raid scenario that can aid in the practical application of game AI. These benchmarks address two open problems in automatic content balancing, and we introduce two evaluation metrics to provide guidance for AI in automatic content balancing. This novel game research platform expands the frontiers of automatic game balancing problems and offers a framework within a realistic game production pipeline.Comment: 14 pages, 6 figures, 6 tables, 2 algorithm

Ludii as a Competition Platform

Ludii is a general game system being developed as part of the ERC-funded Digital Ludeme Project (DLP). While its primary aim is to model, play, and analyse the full range of traditional strategy games, Ludii also has the potential to support a wide range of AI research topics and competitions. This paper describes some of the future competitions and challenges that we intend to run using the Ludii system, highlighting some of its most important aspects that can potentially lead to many algorithm improvements and new avenues of research. We compare and contrast our proposed competition motivations, goals and frameworks against those of existing general game playing competitions, addressing the strengths and weaknesses of each platform

Physical Reasoning for Intelligent Agent in Simulated Environments

Developing Artificial Intelligence (AI) that is capable of understanding and interacting with the real world in a sophisticated way has long been a grand vision of AI. There is an increasing number of AI agents coming into our daily lives and assisting us with various daily tasks ranging from house cleaning to serving food in restaurants. While different tasks have different goals, the domains of the tasks all obey the physical rules (classic Newtonian physics) of the real world. To successfully interact with the physical world, an agent needs to be able to understand its surrounding environment, to predict the consequences of its actions and to draw plans that can achieve a goal without causing any unintended outcomes. Much of AI research over the past decades has been dedicated to specific sub-problems such as machine learning and computer vision, etc. Simply plugging in techniques from these subfields is far from creating a comprehensive AI agent that can work well in a physical environment. Instead, it requires an integration of methods from different AI areas that considers specific conditions and requirements of the physical environment. In this thesis, we identified several capabilities that are essential for AI to interact with the physical world, namely, visual perception, object detection, object tracking, action selection, and structure planning. As the real world is a highly complex environment, we started with developing these capabilities in virtual environments with realistic physics simulations. The central part of our methods is the combination of qualitative reasoning and standard techniques from different AI areas. For the visual perception capability, we developed a method that can infer spatial properties of rectangular objects from their minimum bounding rectangles. For the object detection capability, we developed a method that can detect unknown objects in a structure by reasoning about the stability of the structure. For the object tracking capability, we developed a method that can match perceptually indistinguishable objects in visual observations made before and after a physical impact. This method can identify spatial changes of objects in the physical event, and the result of matching can be used for learning the consequence of the impact. For the action selection capability, we developed a method that solves a hole-in-one problem that requires selecting an action out of an infinite number of actions with unknown consequences. For the structure planning capability, we developed a method that can arrange objects to form a stable and robust structure by reasoning about structural stability and robustness

The perception and cognition of emotion from motion

Emotional expression has been intensively researched in the past, however, this research was normally conducted on facial expressions and only seldomly on dynamic stimuli. We have been interested in better understanding the perception and cognition of emotion from human motion. To this end 11 experiments were conducted that spanned the perception and representation of emotion, the role spatial and temporal cues played in the perception of emotions and finally high level cognitive features in the categorisation of emotion. The stimuli we employed were point-light displays of human arm movements recorded as actors portrayed ordinary actions with emotion. To create them we used motion capture technology and computer animation techniques. Results from the first two experiments showed basic human competence in recognition of emotion and that the representation of emotions is along two dimensions. These dimensions resembled arousal and valence, and the psychological space resembled that found for both facial expression and experienced affect. In a search for possible stimulus properties that would act as correlates for the dimensions, it emerged that arousal could be accounted for by movement speed while valence was related to phase relations between joints in the displays. In the third experiment we manipulated the dimension of arousal and showed that through a modulation of duration, perception of angry, sad and neutral movements could be modulated. In experiments 4-7 the contribution of spatial cues to the perception of emotion was explored and in the final set of experiments (8-11) perception of emotion was examined from a cognitive perspective. Through the course of the research a number of interesting findings emerged that suggested three primary directions for future research: the possible relationship between attributions of animacy and emotion to animate and inanimate non-humans. The phase or timing relationships between elements in a display as a categorical cue to valence and finally the unexplored relationship between cues to emotion from movements and faces

Understanding learning within a commercial video game: A case study

There has been an increasing interest in the debate on the value and relevance using video games for learning. Some of the interest stems from frustration with current educational methods. However, some of this interest also stems from the observations of large numbers of children that play video games. This paper finds that children can learn basic construction skills from playing a video game called World of Goo. The study also employed novel eye-tracking technology to measure endogenous eye blinks and eye gaze fixations. Measures of both these indicators of cognitive processing further suggested that children in the study learned to play the two video games, World of Goo and Bad Piggies. Overall, the results of the study provide further support of the potential for children to learn by playing commercial video games

The Gamut: A Journal of Ideas and Information, No. 21, Summer 1987

CONTENTS OF ISSUE NO. 21, SUMMER, 1987 Louis T Milic: Editorial, 3 The North Coast? James and Susan Borchert: The Bird\u27s Nest, 4 The making of an ethnic urban village Ethna Carroll: Fiction: The Mortal Cauliflower, 14 Special Section The Great Lakes Michael J. Tevesz Samuel M. Savin: Lake Shores in Retreat, 21 Interference with natural erosion could cause worse problems Michael T Gavin: The Great Lakes Exposition of 1936, 37 Fifty years ago, Cleveland\u27s lakefront was a spectacular showplace Alan MacDougall: Inland Sailor: Poems and Photographs, 44 Thirteen years on the ore boats Kristin Blumberg: Proton Decay, 51 Scientists wait 2000 feet underground to witness an event that may never take place Emily Cain: Lake Ontario\u27s Time Capsule, 59 Sunken ships miraculously preserved since 1812 Thomas Lewis: The Making of Lake Erie, 64 How glaciers carved out Cleveland\u27s watery neighbor Timothy Runyan: Redeveloping the Cleveland Lakefront, 74 The Inner Harbor project struggles to a start Oliver C. Schroeder, Jr.: Antarctica: New Laws for a New Land, 81 The Antarctica treaty. A model of international cooperation, is due for renewalhttps://engagedscholarship.csuohio.edu/gamut_archives/1018/thumbnail.jp