Procedural Content Generation for Real-Time Strategy Games

Videogames are one of the most important and profitable sectors in the industry of entertainment. Nowadays, the creation of a videogame is often a large-scale endeavor and bears many similarities with, e.g., movie production. On the central tasks in the development of a videogame is content generation, namely the definition of maps, terrains, non-player characters (NPCs) and other graphical, musical and AI-related components of the game. Such generation is costly due to its complexity, the great amount of work required and the need of specialized manpower. Hence the relevance of optimizing the process and alleviating costs. In this sense, procedural content generation (PCG) comes in handy as a means of reducing costs by using algorithmic techniques to automatically generate some game contents. PCG also provides advantages in terms of player experience since the contents generated are typically not fixed but can vary in different playing sessions, and can even adapt to the player herself. For this purpose, the underlying algorithmic technique used for PCG must be also flexible and adaptable. This is the case of computational intelligence in general and evolutionary algorithms in particular. In this work we shall provide an overview of the use of evolutionary intelligence for PCG, with special emphasis on its use within the context of real-time strategy games. We shall show how these techniques can address both playability and aesthetics, as well as improving the game AI

Inside the brain of an elite athlete: The neural processes that support high achievement in sports

Events like the World Championships in athletics and the Olympic Games raise the public profile of competitive sports. They may also leave us wondering what sets the competitors in these events apart from those of us who simply watch. Here we attempt to link neural and cognitive processes that have been found to be important for elite performance with computational and physiological theories inspired by much simpler laboratory tasks. In this way we hope to inspire neuroscientists to consider how their basic research might help to explain sporting skill at the highest levels of performance

A Survey of Monte Carlo Tree Search Methods

Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

The design and implementation of an elite training system for tennis

Thesis (Ed.D.)--Boston UniversityPLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at [email protected]. Thank you.The purpose of this study was to design a framework for a tennis training system that can be used to develop young tennis players into elite, world-class professionals. An elite tennis player is defined as someone who ultimately attains a top 200 professional ranking on either the men's ATP Tour or the women's WTA Tour. This system is meant to be utilized by training institutions as a whole, as well as individual players and their coaches. The methodology involved a literature review of research in youth talent development and talent detection, with emphasis on the works of Bloom (1985) and Ericsson (1990, 1993, 1994, 1996). Interviews were also conducted with prominent independent tennis coaches like Nick Bollettieri and Robert Lansdorp, as well as with a private coach from Estonia, and coaches from the tennis federation's of France and the Czech Republic. The results found that tennis development should begin between the ages of six to eight. In the early stages of development it was found that fun, stroke technique, and learning how to play matches should be emphasized. Sport specialization should not occur before age 13 or 14. In order to attain expert performance players should amass 10 years and 10,000 hours of directed deliberate practice. Although researchers theorized that players do not benefit from practices lasting longer than four hours, coaches interviewed believe that it is essential to practice for five to six hours between the ages of 16-18 when the player is transitioning from junior tennis to professional tennis. A nurturing relationship with a coach and supportive, yet moderately involved parents, are also key elements to this development program. Most importantly, players must develop and maintain a love for tennis if they are to attain expert status.2031-01-0

Fifth Aeon – A.I Competition and Balancer

Collectible Card Games (CCG) are one of the most popular types of games in both digital and physical space. Despite their popularity, there is a great deal of room for exploration into the application of artificial intelligence in order to enhance CCG gameplay and development. This paper presents Fifth Aeon a novel and open source CCG built to run in browsers and two A.I applications built upon Fifth Aeon. The first application is an artificial intelligence competition run on the Fifth Aeon game. The second is an automatic balancing system capable of helping a designer create new cards that do not upset the balance of an existing collectible card game. The submissions to the A.I competition include one that plays substantially better than the existing Fifth Aeon A.I with a higher winrate across multiple game formats. The balancer system also demonstrates an ability to automatically balance several types of cards against a wide variety of parameters. These results help pave the way to cheaper CCG development with more compelling A.I opponents

Computability and Evolutionary Complexity: Markets As Complex Adaptive Systems (CAS)

The purpose of this Feature is to critically examine and to contribute to the burgeoning multi disciplinary literature on markets as complex adaptive systems (CAS). Three economists, Robert Axtell, Steven Durlauf and Arthur Robson who have distinguished themselves as pioneers in different aspects of how the thesis of evolutionary complexity pertains to market environments have contributed to this special issue. Axtell is concerned about the procedural aspects of attaining market equilibria in a decentralized setting and argues that principles on the complexity of feasible computation should rule in or out widely held models such as the Walrasian one. Robson puts forward the hypothesis called the Red Queen principle, well known from evolutionary biology, as a possible explanation for the evolution of complexity itself. Durlauf examines some of the claims that have been made in the name of complex systems theory to see whether these present testable hypothesis for economic models. My overview aims to use the wider literature on complex systems to provide a conceptual framework within which to discuss the issues raised for Economics in the above contributions and elsewhere. In particular, some assessment will be made on the extent to which modern complex systems theory and its application to markets as CAS constitutes a paradigm shift from more mainstream economic analysis

Evolutionary design of deep neural networks

Mención Internacional en el título de doctorFor three decades, neuroevolution has applied evolutionary computation to the optimization of the topology of artificial neural networks, with most works focusing on very simple architectures. However, times have changed, and nowadays convolutional neural networks are the industry and academia standard for solving a variety of problems, many of which remained unsolved before the discovery of this kind of networks. Convolutional neural networks involve complex topologies, and the manual design of these topologies for solving a problem at hand is expensive and inefficient. In this thesis, our aim is to use neuroevolution in order to evolve the architecture of convolutional neural networks. To do so, we have decided to try two different techniques: genetic algorithms and grammatical evolution. We have implemented a niching scheme for preserving the genetic diversity, in order to ease the construction of ensembles of neural networks. These techniques have been validated against the MNIST database for handwritten digit recognition, achieving a test error rate of 0.28%, and the OPPORTUNITY data set for human activity recognition, attaining an F1 score of 0.9275. Both results have proven very competitive when compared with the state of the art. Also, in all cases, ensembles have proven to perform better than individual models. Later, the topologies learned for MNIST were tested on EMNIST, a database recently introduced in 2017, which includes more samples and a set of letters for character recognition. Results have shown that the topologies optimized for MNIST perform well on EMNIST, proving that architectures can be reused across domains with similar characteristics. In summary, neuroevolution is an effective approach for automatically designing topologies for convolutional neural networks. However, it still remains as an unexplored field due to hardware limitations. Current advances, however, should constitute the fuel that empowers the emergence of this field, and further research should start as of today.This Ph.D. dissertation has been partially supported by the Spanish Ministry of Education, Culture and Sports under FPU fellowship with identifier FPU13/03917. This research stay has been partially co-funded by the Spanish Ministry of Education, Culture and Sports under FPU short stay grant with identifier EST15/00260.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: María Araceli Sanchís de Miguel.- Secretario: Francisco Javier Segovia Pérez.- Vocal: Simon Luca

On the evolution of growth and senescence /

PhD ThesisConsistent associations between growth and senescence are seen throughout nature. Whilst a larger size correlates positively with lifespan between species, this relationship is reversed within a species so that the smallest members tend to be the longest‐lived. Indeterminate growth ‐ i.e. growth that continues post‐maturity ‐ is a strong predictor for an especially slow rate of ageing. A number of interventions which alter the rate of growth, especially at a point early in development, have been shown to have enduring effects on later growth and lifespan. This thesis provides a theoretical examination of why relationships such as these may have evolved. Two dynamic programming models are here presented. Both consider associations between growth and longevity within a species and ask whether these are compatible with idea of a trade‐off between somatic maintenance and other fitness‐enhancing functions as predicted by the disposable soma theory. The first reproduces the sexual dimorphism in longevity and in body size seen baboons; it predicts that males should ‘choose’ a faster rate of ageing and a greater investment in growth than females. The second suggests that a faster rate of ageing may be an optimal response to low food availability in early life in humans. A critical appraisal is also given to two recent theories of the evolution of ageing which rely explicitly on differences in body size and/or growth to explain differences in lifespan: the hyperfunction theory and the heat dissipation limit theory. What these can teach us about the evolution of senescence and whether they can provide plausible challenges to the disposable soma theory is considered.BBSRC

Unemployment Insurance and the Evolution of Worker-Employer\n Cooperation: Experiments with Real and Artificial Agents

This paper reports the results of human subject and computational experiments designed to examine how the level of the "inactivity payments" to workers and to employers affects the evolution of cooperation among workers and employers. The related impacts to unemployment and job vacancy rates are our primary focus. However, we also examine the impacts on labor force participation, productive efficiency, the willingness to form long term relationships, and other outcome measures.Agent-based computational economics; Labor market; Unemployment\n benefits; Evolution of cooperation; Adaptive search