**1. Introduction**

Real-Time Strategy (RTS) games are designed as turn-based games where players, each following their own strategies, try to defeat one another through a series of turns. The term 'strategy' stands for the highest form of decision-making process, where the ultimate purpose is to defeat the opponent. Decisions are made between turns (a turn is a transition from the current game state to the next one), which are so short (i.e., in the range of milliseconds) that the game looks as though it is progressing in real time. After a decision is made, the actions are executed. The di fference between RTS games and classical turn-based board games, of which probably the most well-known representative is the game of chess, is in the execution of the actions. Actions in RTS games are durative and simultaneous [1], as opposed to the instant moves, of which each player can make one per turn, in classical board games.

During the last decade, RTS games have become one of the best test beds for researching Artificial Intelligence (AI) for games [2,3]. The main reason for the growth in research is the fact that RTS games offer plenty of challenges for researchers. For example, RTS games are representatives of the highest class of computational complexity [4], which is due to their extremely large state-action spaces [5] (i.e., search space). Search space is often impossible to search exhaustively, because a specific game is a high-dimensional space of game variants (many di fferent parameters are available), and it is also called game space [6].

Exploring the search space of games is often considered to be a di fficult problem [7], and most of the complex optimization problems relating to games' search spaces cannot be solved using the exact methods that search for the optimal solution by enumerating all possible solutions. To solve these problems, various methods have emerged in the past decades that solve problems approximately. In recent times, researchers have been looking for inspiration for the design of these approximate algorithms/methods in nature, e.g., Darwin's evolutionary theory [8], the collective behavior of social living insects [9], the social behavior of some animal species [10,11], physical phenomena [12], and so on.

The bio-inspired computation field [13] is a field that covers all of the algorithms/methods that fall within the scope of these mentioned inspirations and is an extensively studied research area of AI. Nowadays, numerous algorithms exist that fall under the bio-inspired computation umbrella, such as the Artificial Bee Colony (ABC) Algorithm [14], Di fferential Evolution (DE) [15], Firefly Algorithm (FA), Genetic Algorithm (GA) [16], Monarch Butterfly Optimization (MBO) [17], etc. Due to the popularity of this subject, numerous unprecedented implications of these approaches exist among real-world applications [13]. Some of the application areas where bio-inspired computation approaches have been successfully applied include: antenna design [18], medicine [19], and dynamic data stream clustering [20].

In addition to the many di fferent application areas, bio-inspired computation also plays an important role in the design and development of games. Bio-inspired computation approaches in games have been used for procedural content generation [21], the development of controllers that are able to play games [22], educational and serious games [23], intelligent gaming systems [24], evolutionary methods in board games [25], behavioral design of non-player characters [26], etc.

Gameplaying agents (algorithms) are made to play the game in question, with the game rules being hard-coded or self-obtained (general gameplaying), in a self-sustained way (i.e., no human input is needed during the (general) gameplay) [27]. The primary task of the gameplaying agen<sup>t</sup> is to win games, and the secondary task is to win them with a higher score [28]. For the RTS gameplaying agen<sup>t</sup> [29] to be able to cope with the high computational complexity of the game space, it has to be able to function inside di fferent segments of the game, which are as follows: resource and production managemen<sup>t</sup> (also categorized as economy) [30], strategical [31], tactical [32] and micromanagement [33] operations, scouting [34] and sometimes even diplomacy [35]. For one to be successful when playing an RTS game, a balanced combination of all those segments must be considered by the agen<sup>t</sup> [36]. Since gameplaying agents are already built to operate and cover a variety of tasks in a given game space, they can be adapted to become playtesting agents.

Playtesting agents are meant to play through the game (or a slice of it) and try to explore the behavior that can generate data that would assist developers during the development phase of a game [37,38]. Game studios conduct countless tests on gameplaying with real players [39], but relying on humans for playtesting can result in higher costs and can also be ine fficient [37]. The research on playtesting is, therefore, very important for the following two reasons: it has a huge economic potential and is of considerable interest to the game industry [40]. Further economic potential is also apparent in semi-related fields, like Gamification [41].

A Game Design Document (GDC) specifies core gameplay, game elements, necessary game features, etc. [42]. With this paper, we tackle the problem of the automatic validation of game features for the game space specified in GDC and also address research requirements from articles (for instance, [43]), where the authors point out the need that games with a higher complexity have of scaling.

In this article, we will try to find the answers to the following research questions:


Altogether, the main contributions of this paper are as follows:


The structure of the remainder of this paper is as follows. Section 2 outlines the game features of real-time strategy games and the microRTS simulation environment, while Section 3 presents the proposed novel metric that will allow for the comparison of di fferent playtesting agents. Section 4 describes the experimental environment, adaptation of gameplaying agents as playtesting agents (including detailed descriptions of them) and the results of the experiments. A Discussion is provided in Section 5, and the conclusion is presented in Section 6.

#### **2. Real-Time Strategy Games**

This chapter briefly outlines the game features of RTS games, and a description of the microRTS environment is also provided.

#### *2.1. Game Features of RTS Games*

"Game feature" is a generic term used to refer to di fferences and similarities between games [45]. Game features are defined in GDC [46], and, after they are implemented, each game's features rely on the use of game mechanics. Game mechanics are methods invoked by agents in interacting with the game world (e.g., to obtain the health value of the unit) [47]. In [48], 18 general definitions of game features (hereinafter referred to as groups) can be found.

In the RTS game domain, di fferent kinds of game feature subset groupings are possible (Economic, Military, Map Coverage, Micro Skill and Macro Skill) [49], but to the best of our knowledge, the RTS game features have not ye<sup>t</sup> been placed into groups. The placement of RTS game features into groups is, in our opinion, important, because it allows for the possibility of comparing RTS game features with the features of other game genres in the future.
