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

Mind over machine : what Deep Blue taught us about chess, artificial intelligence, and the human spirit

Thesis (S.M. in Science Writing)--Massachusetts Institute of Technology, Dept. of Humanities, Graduate Program in Science Writing, 2007."September 2007."Includes bibliographical references (leaves 44-49).On May 11th 1997, the world watched as IBM's chess-playing computer Deep Blue defeated world chess champion Garry Kasparov in a six-game match. The reverberations of that contest touched people, and computers, around the world. At the time, it was difficult to assess the historical significance of the moment, but ten years after the fact, we can take a fresh look at the meaning of the computer's victory. With hindsight, we can see how Deep Blue impacted the chess community and influenced the fields of philosophy, artificial intelligence, and computer science in the long run. For the average person, Deep Blue embodied many of our misgivings about computers becoming our new partners in the information age. For researchers in the field it was emblematic of the growing pains experienced by the evolving field of AI over the previous half century. In the end, what might have seemed like a definitive, earth-shattering event was really the next step in our on-going journey toward understanding mind and machine. While Deep Blue was a milestone - the end of a long struggle to build a masterful chess machine - it was also a jumping off point for other lines of inquiry from new supercomputing projects to the further development of programs that play other games, such as Go. Ultimately, the lesson of Deep Blue's victory is that we will continue to accomplish technological feats we thought impossible just a few decades before. And as we reach each new goalpost, we will acclimate to our new position, recognize the next set of challenges before us, and push on toward the next target.by Barbara Christine Hoekenga.S.M.in Science Writin

Towards the unification of intuitive and formal game concepts with applications to computer chess

In computer game development, an interesting point which has been little or no studied at all is the formalization of intuition such as game playing concepts, including playing style. This work is devoted to bridge the gap between human reasoning in game playing and heuristic game playing algorithms. The idea is motivated as follows. In most chess-like games there exist many intuition-oriented concepts such as capture, attack, defence, threaten, blocked position, sacrifice, zugzwang position and different playing styles such as aggressive, conservative, tactical and positional. Most human players use to manage these concepts, pergaps in an intuitive way, as they were not well formalized in a precise manner. A good formalization of these concepts would be an important step towards the automation of human reasoning in chess (and other strategy games) for better understanding of the game, thus leading to better playing. The goal of this research is to take a first step towards the unification of both "paradigms", namely human reasoning in game play and more formal heuristic concepts. We focus on computer chess as an example but the result could be also applied to most two-player zero-sum perfect information games. The applications of such a formulation are practical, such as better game understanding and opponent modeling, as well as educational: it would be nice to have these concepts somehow formalized. Then we suggest a way of transfering these intuitions into formal definitions. We propose an interpretation technique for describing chess positions and evaluation functions. The technique consists of interpreting and mapping part of the algorithmic scenario into quantities such as integer numbers. With such a mapping a given concept is likely to be described in a very precise way. As an application we look for candidate definitions of the following concepts: attack, defence, threat, sacrifice, zugzwang, aggressive play and defensive play. For each one of them we use the previous technique and propose a formal definition. Thus we give the first formulation of game playing styles -at least to the author\u27s knowledge- and we show how this definition goes through for the game of chess. We describe different possibilities when moving from intuition to the formal setting, varying from a simple formulation through a connectionist approach. Then we show as an application how an evaluation function can be modified in order to include a given concept. This new evaluation function should take into account the degree of presence of the given concept (eg. how defensive is a given position) and thus it can be incorporated into a computer chess program. An advantage of allowing one to modify in such a manner an evaluation function is that one can combine different evaluation functions and -perhaps- get the better of each one of them. Although this is a first step in the given direction, some more difficult tasks will remain, such as the formalization of the so called positional, strategic and tactical play. References B. Abramson. Learning expected-outcome evaluators in chess. In H. Berliner, editor, Proceedings of the AAAI Spring Symposium on Computer Game Playing, pages 26-28, Stanford University, 1988. B. Abramson. On learning and testing evaluation functions. Journal of Experimental and Theoretical Artificial Intelligence, 2(3):182-193, 1990. T. S. Anantharaman. Evaluation tuning for computer chess: Linear discriminant methods. International Computer Chess Association Journal, 20(4):224-242, 1997. E. B. Baum, Warren D. Smith. Best Play for Imperfect Players and Game Tree Search. 1993 J. Fürnkranz. Machine Learning in Computer Chess: The Next Generation Austrian Research Institute for Artificial Intelligence, Vienna, TR-96-11, 1996. A. Plaat, J. Schaeffer, W. Pijls and A. De Bruin. Best-First Fixed-Depth Game-Tree Search in Practice. IJCAI\u2795, Montreal. J. Schaeffer, P. Lu, D. Szafron and R. Lake. A Re-examination of Brute-Force Search Games: Planning and Learning, Chapel Hill, N.C., pp. 51-58, 1993. AAAI Report FS9302

Using machine learning techniques to create AI controlled players for video games

This study aims to achieve higher replay and entertainment value in a game through human-like AI behaviour in computer controlled characters called bats. In order to achieve that, an artificial intelligence system capable of learning from observation of human player play was developed. The artificial intelligence system makes use of machine learning capabilities to control the state change mechanism of the bot. The implemented system was tested by an audience of gamers and compared against bats controlled by static scripts. The data collected was focused on qualitative aspects of replay and entertainment value of the game and subjected to quantitative analysi

A multivariate and cluster analysis of diverse playing styles across European football leagues

Performance analysis is a valuable tool for team coaches and has been the subject of extensive study in international research. A significant portion of the scientific literature in the field of football has been devoted to studying playing styles in recent years. The identification of playing styles is now regarded as crucial for conducting an efficient performance analysis. This study aimed to explore the variances in playing styles among eleven distinct European domestic football leagues. A comprehensive sample of 2996 matches, accounting for 5992 observations, was scrutinized. Nineteen latent variables, representing thirty-eight different game styles previously identified in sports science literature, served as the basis for this investigation. Multivariate analysis of variance (MANOVA) revealed significant differences across countries in ten out of nineteen variables. The variables with the highest effect sizes (partial η2) were transition game, effective game, and defending aggressively, implying that these factors contributed to the most substantial differences among countries. To visualize these disparities, the t-distributed stochastic neighbor embedding (t-SNE) method was employed. Subsequently, k-means clustering was applied to the t-SNE results, grouping the eleven participating countries into five distinct clusters. A unique playing style was discerned in the Scottish league (Cluster 4), setting it apart from all other leagues. Other clusters included Austria, Belgium, and Switzerland (Cluster 1); Spain, Turkey, and Croatia (Cluster 2); Greece and Italy (Cluster 3); and Germany and England (Cluster 5). The findings offer valuable insights for coaches, managers, scouts, and sporting directors, potentially guiding the development of effective game styles and enhancing recruitment strategies for both players and coaches

Football analytics: a literature analysis from 2010 to 2020

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe overall goal for the current study is to present a literature review of analytics, precisely machine learning (ML) reference authors in terms of methods and applicable scopes of study, in football where is a field that historically there are empirical decisions and the usage of analytics has been growing intensely. The research aims to list relevant academic contributions published between 2010 and 2020, performing a comparable picture per authors across the following subsets: player individual technical skills and team performance. Furthermore, the approach will provide a summary of studies for machine learning methods applied in football. Such outcomes of this study would contribute to the discussion about football analytics. Regarding that these summaries can drive researchers to have a deep dive into the fields of interest straight to references preview studied in the thesis. Results indicate that football analytics has broadly vast opportunities in terms of research, regarding machine learning methods and a high potential to have a deep exploration of team and player perspective. This study can leverage and pavement new further in-depth and targeted investigation toward football analytics

Temporal Difference Learning in Complex Domains

PhDThis thesis adapts and improves on the methods of TD(k) (Sutton 1988) that were successfully used for backgammon (Tesauro 1994) and applies them to other complex games that are less amenable to simple pattem-matching approaches. The games investigated are chess and shogi, both of which (unlike backgammon) require significant amounts of computational effort to be expended on search in order to achieve expert play. The improved methods are also tested in a non-game domain. In the chess domain, the adapted TD(k) method is shown to successfully learn the relative values of the pieces, and matches using these learnt piece values indicate that they perform at least as well as piece values widely quoted in elementary chess books. The adapted TD(X) method is also shown to work well in shogi, considered by many researchers to be the next challenge for computer game-playing, and for which there is no standardised set of piece values. An original method to automatically set and adjust the major control parameters used by TD(k) is presented. The main performance advantage comes from the learning rate adjustment, which is based on a new concept called temporal coherence. Experiments in both chess and a random-walk domain show that the temporal coherence algorithm produces both faster learning and more stable values than both human-chosen parameters and an earlier method for learning rate adjustment. The methods presented in this thesis allow programs to learn with as little input of external knowledge as possible, exploring the domain on their own rather than by being taught. Further experiments show that the method is capable of handling many hundreds of weights, and that it is not necessary to perform deep searches during the leaming phase in order to learn effective weight

The offensive patterns causing disequilibrium in the defensive organization of the opponent leading to a goal scored in soccer

This study has an interest to understand and detect the offensive patterns of the most 2 teams with highest goal scored per game in the top 5 leagues and the effect on creating disequilibrium on the opponentopponent’s defensive lines. Thus, this allows to identify the interactions of the players between their teammates and their opponent. 76 out of 99 goals for Bayern Munich and 67 out of 90 goals for Atalanta were observed and analyzed using REOFUT protocol. Some similarities were detected between both teams using Chi Square Test to discover the association between different variables like Initial opponent behavior and Type of attack, Penultimate action and Penultimate invasive zone, Last action and Penultimate action with, X^2= 15.005, P=0.05, X^2= 31.932, P=0.006 X^2= 40.920, P= < respectively for Bayern Munich and X^2= 14.983a, P=0.045, X^2= 24.945a, P=0.034 and X^2= 20.696a, P=0.015, respectively for Atalanta As a conclusion, although the detection of the correlation between both team and opponentopponent’s behavior, number, pressure and space, various factors influence the patterns and playing dynamics which were not mentioned all in this study.Este estudo tem como objetivo compreender e detetar os pa drões ofensivos das duas equipas com maior número de golos marcados por jogo nas 5 principais ligas e o efeito na criação de desequilíbrio nas linhas defensivas do adversário. Com isso, torna se possível identificar as interações dos jogadores entre seus c ompanheiros e adversários. 76 de 99 golos do Bayern de Munique e 67 de 90 golos do Atalanta foram observados e analisados usando o protocolo REOFUT. Algumas semelhanças foram detectadas entre as equipas usando o Teste Qui Quadrado para descobrir a associ ação entre diferentes variáveis como comportamento inicial do oponente e tipo de ataque, penúltima ação e penúltima zona invasiva, última ação e penúltima ação com X^2= 15.005, P=0.05, X^2= 31.932, P=0.006 e X^2= 40.920, P= < respectivamente para o Bayern Munich e X^2= 14.983a, P=0.045, X^2= 24.945a, P=0.034 e X^2= 20.696a, P=0.015, respectivamente para o Atalanta Como conclusão, embora a detecção da correlação entre o comportamento da equipa e do adversário, número, pressão e espaço, vários fato res influenciam os padrões e a dinâmica de jogo que não foram mencionados neste estudo

Suspicion-Agent: Playing Imperfect Information Games with Theory of Mind Aware GPT-4

Unlike perfect information games, where all elements are known to every player, imperfect information games emulate the real-world complexities of decision-making under uncertain or incomplete information. GPT-4, the recent breakthrough in large language models (LLMs) trained on massive passive data, is notable for its knowledge retrieval and reasoning abilities. This paper delves into the applicability of GPT-4's learned knowledge for imperfect information games. To achieve this, we introduce \textbf{Suspicion-Agent}, an innovative agent that leverages GPT-4's capabilities for performing in imperfect information games. With proper prompt engineering to achieve different functions, Suspicion-Agent based on GPT-4 demonstrates remarkable adaptability across a range of imperfect information card games. Importantly, GPT-4 displays a strong high-order theory of mind (ToM) capacity, meaning it can understand others and intentionally impact others' behavior. Leveraging this, we design a planning strategy that enables GPT-4 to competently play against different opponents, adapting its gameplay style as needed, while requiring only the game rules and descriptions of observations as input. In the experiments, we qualitatively showcase the capabilities of Suspicion-Agent across three different imperfect information games and then quantitatively evaluate it in Leduc Hold'em. The results show that Suspicion-Agent can potentially outperform traditional algorithms designed for imperfect information games, without any specialized training or examples. In order to encourage and foster deeper insights within the community, we make our game-related data publicly available