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This paper describes the application of competitive coevolution as a mechanism of self learning in a two-player real time strategy (RTS) game. The paper presents this (war) RTS game, developed by the authors as an open-source tool, and describes its (built-in) coevolutionary engine developed to find winning strategies. This engine applies a competi...
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Citations
... This can be seen as increasing the fitness values of one species leading to improved fitness values of the other species. In contrast, competitive coevolution takes place when the shared information affects the receiving population's fitness function negatively [20], [52], [62]. ...
... Nogueira et al. [52] started a series of studies; initially they introduced a new RTS game called "RobotWars". This game includes two components: a battle generator to generate different scenarios, and a battle simulator to run the game. ...
... A year later, they [51] extended their previous work [52] to analyse five different HoF implementations for short/long term memory mechanisms. They found that updating the memory members using selection criteria produced a good performance, noting that diversity was the best selection criterion to be used. ...
Real-time strategy (RTS) games are a subgenre of strategy video games. Due to their importance in practical decision-making and digital entertainment over the last two decades, many researchers have explored different algorithmic approaches for controlling agents within RTS games and learning effective strategies and tactics. Among the techniques, coevolutionary algorithms proved to be one of the most popular and successful algorithms for developing such games, in which players can compete or cooperate to achieve the given game’s mission. However, as many alternative designs exist with their analysis and the applications reported in diverse publications, a review covering the evolution of such algorithms would be valuable for researchers and practitioners in this domain. This paper aims to provide a systematic review by highlighting why and how coevolution is used in RTS games and analysis of the recent work. The review conducted follows procedural steps to identify, filter, analyse and discuss the existing literature. This structured review articulates the purposes of using coevolution in RTS games and highlights several open questions for future research in this domain.
... Our first works [28], [29] were conducted in the context of the RTS game RobotWars 7 . The main goal was generating game strategies to control the behavior of an army. ...
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.
... The work presented here, builds on our previous work [30,31] on testing the use of HoF-based competitive coevolutionary (HoFCC) algorithms for finding winning strategies in RTS games. In our previous papers we analyzed how the diversity and the growth of the HoF can influence the quality of the solutions obtained by HoFCC algorithms. ...
... However, although the contribution of the "archive methods" to reduce the number of drawbacks (e.g., missing features and cycling) of coevolutionary systems is undeniable, their implementation entails a number of issues that have to be taken into account such as the strength of the archive members and the control of the archive diversity. So for instance, a high risk of generating cycles can appear if the strength of a member is valued by pitting this member against a fixed set of opponents, and this is precisely what we did in our previous work [30,31]. A key point to consider is the metric that defines the strength of an individual, which should indicate when an individual is qualitatively superior to the rest but without losing sight of the fact that sometimes quantitative superiority does not correspond with a high strength. ...
... Using Planet Wars we propose, in this paper, a modified and improved version of a competitive coevolutionary algorithm that we already described in [30,31] for a simplistic capture-the-flag game. The latter used the HoF as a long-term memory mechanism to keep the winning strategies found in each coevolutionary step and all of them were also used in the evaluation process (in the basic algorithm). ...
The Google Artificial Intelligence (AI) Challenge is an international contest the objective of which is to program the AI in a two-player real time strategy (RTS) game. This AI is an autonomous computer program that governs the actions that one of the two players executes during the game according to the state of play. The entries are evaluated via a competition mechanism consisting of two-player rounds where each entry is tested against others. This paper describes the use of competitive coevolutionary (CC) algorithms for the automatic generation of winning game strategies in Planet Wars, the RTS game associated with the 2010 contest. Three different versions of a prime algorithm have been tested. Their common nexus is not only the use of a Hall-of-Fame (HoF) to keep note of the winners of past coevolutions but also the employment of an archive of experienced players, termed the hall-of-celebrities (HoC), that puts pressure on the optimization process and guides the search to increase the strength of the solutions; their differences come from the periodical updating of the HoF on the basis of quality and diversity metrics. The goal is to optimize the AI by means of a self-learning process guided by coevolutionary search and competitive evaluation. An empirical study on the performance of a number of variants of the proposed algorithms is described and a statistical analysis of the results is conducted. In addition to the attainment of competitive bots we also conclude that the incorporation of the HoC inside the primary algorithm helps to reduce the effects of cycling caused by the use of HoF in CC algorithms.
... This section is devoted to RobotWars 1 , our arena for competitive coevolution which will be presented for first time in [18]. RobotWars is a test environment that allows two virtual players (i.e. ...
This paper explores the use of Hall-of-Fame (HoF) in the application of competitive coevolution for finding winning strategies in RobotWars, a two-player real time strategy (RTS) game developed in the University of Malaga for research purposes. The main goal is testing different approaches in order to implement the concept of HoF as part of the self learning mechanism in competitive coevolutionary algorithms. Five approaches were designed and tested, the difference between them being based on the implementation of HoF as a long or short-term memory mechanism. Specifically they differ on the police followed to keep the members in the champions' memory during an updating process which deletes the weakest individuals, in order to consider only the robust members in the evaluation phase. It is shown how strategies based on periodical update of the HoF set on the basis of quality and diversity provide globally better results.
