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Player Skill Modeling in Starcraft II Proceedings of the Ninth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Player Skill Modeling in Starcraft II Tetske Avontuur, Pieter Spronck, and Menno van Zaanen Tilburg Center for Cognition and Communication Tilburg University, The Netherlands Abstract during gameplay, the leagues to which Starcraft II players are assigned. We are particularly interested in determining Starcraft II is a popular real-time strategy (RTS) game, in the player’s league as early in the game as possible, to al- which players compete with each other online. Based on their performance, the players are ranked in one of seven leagues. low an AI opponent to adapt its tactics and strategies to the In our research, we aim at constructing a player model that is player’s level before in-game confrontations with the player capable of predicting the league in which a player competes, have taken place. using observations of their in-game behavior. Based on cog- nitive research and our knowledge of the game, we extracted Background from 1297 game replays a number of features that describe We discuss being an expert in chess playing, and compare skill. After a preliminary test, we selected the SMO classi- this to being an expert in video games. We then discuss fier to construct a player model, which achieved a weighted accuracy of 47.3% (SD = 2.2). This constitutes a signific- player modeling in general, and describe Starcraft II and our ant improvement over the weighted baseline of 25.5% (SD = reasons for choosing the game for the present research. 1.1). We tested from what moment in the game it is possible to predict a player’s skill, which we found is after about 2.5 Expertise in chess playing minutes of gameplay, i.e., even before the players have con- With respect to the thought processes of chess players, fronted each other within the game. We conclude that our research shows no difference in depth of search between model can predict a player’s skill early in the game. chess masters and grandmasters, although the latter pro- duce move of higher quality and are better at remembering Introduction non-random board configurations (Campitelli et al. 2007; Reingold et al. 2001). In competitive computer gaming, also called eSports, play- Chase and Simon (1973) found that expert chess play- ers of a game participate in tournaments to determine who ers were better at remembering meaningful board configura- has mastered the game the most. In real-time strategy (RTS) tions, while there was no difference in performance between games, a popular game used for eSports is Starcraft II (Bliz- experts and novices concerning meaningless configurations. zard Entertainment, 2010). The online game divides players Experts were also better at reproducing the position of a into 7 distinct leagues, based on their performance against chess piece after viewing it for five seconds. This research other players. We therefore assume that a player’s league is lead to the chunking theory: the idea that experts make de- a good representation of their level of skill. cisions based on a large number of chunks of information Skill differences between novices and experts have been that are stored in their long term memory, helping them to researched in several domains. Studies in chess suggest that recognize patterns and make quicker decisions. because of their extensive knowledge, experts are strong at Gobet and Simon (1998) extended this theory into the recognizing patterns, make quick decisions based on ob- template theory where a set of chunks forms a complex served patterns, and are able to make effective general as- structure in memory, which allows grandmasters to mem- sumptions based on chunks of information (Gobet and Si- orize relevant information, recognize board positions, and mon 1998). Research with airplane pilots and athletes shows consequently make fast decisions. Gobet and Simon (1996) that experts see related cues faster, make fewer mistakes, and also studied grandmasters playing six opponents simultan- pay less attention to unrelated cues than novices (Schriver et eously. They found that grandmasters reduce the search al. 2008; Chaddock et al. 2011). space by using recognition patterns, based on their extensive To examine how the differences between Starcraft II play- knowledge of the game and their opponents. Campitelli and ers of different skill levels affect gameplay, we created a Gobet (2004) confirmed their findings. player model focused on skills (van den Herik, Donkers, and Spronck 2005). Our goal is to accurately distinguish, Expertise in video games Copyright c 2013, Association for the Advancement of Artificial An important difference between chess and modern video Intelligence (www.aaai.org). All rights reserved. games is ‘pacing’ (the number of actions per time unit), 2 which generally is much higher and frantic for video games. ent playstyles as so-called “play-personas”. Van Lankveld Green and Bavelier (2006; 2007) examined the difference in et al. (2011) correlated the results of the NEO-PI-R test with the allocation of attention between video gamers and non- the gameplay behavior of 80 players of a Neverwinter Nights gamers. They conducted a functional field-of-view task to module. And recently, Tekofsky et al. (2013) sought to build measure how well a person can locate a central task whilst a psychological profile of 13,000 Battlefield 3 players based being distracted by a number of visible elements and one on their playstyle. other central task. The gamers were better able to enumerate and track several simultaneously-moving stimuli over time. Starcraft II They also had better visuospatial attention, allowing them to The game used in this research is Starcraft II: Wings of effectively localize one or two central tasks among a num- Liberty (from hereon referred to as Starcraft II). It is a real- ber of distractions (Green and Bavelier 2007). When non- time strategy game where the players’ goal is to destroy their gamers were asked to play an action game for 10 hours, they enemy’s base by developing their own base and an army. showed significant improvement in attentional resources and Players can choose from three different races to play, each visuospatial attention. In other words, experience with a of which plays very differently. To construct buildings and game seems to improve the players’ ability to multi-task. produce army units, a player needs minerals and gas. Dur- Dye, Green, and Bavelier (2009) examined the allocation of ing the game, players unlock new options by constructing attention to a number of alerting cues. They found that ac- particular buildings. tion gamers responded quicker to such events and attended To play the game well, the player must engage in both to them more accurately than non-gamers. macro and micro-management. Macro management determ- ines the economic strength of a player, represented by the Player modeling construction of buildings, the gathering of resources and the Bakkes, Spronck, and van Lankveld (2012) define a player composition of units. Micro-management determines how model as an abstracted description of a player in a game en- well a player is able to control small groups and individual vironment. A player model may encompass characteristics units, including movements and attacks. A player’s success such as preferences, strategies, strengths, weaknesses, and depends heavily on the strategy followed. Strategic choices skills (van den Herik, Donkers, and Spronck 2005). include finding a balance between building a strong eco- Research into player models for classic board games ori- nomy and building a strong fighting force. ginated with Carmel and Markovitch (1993) and Iida et al. Using Blizzard’s multiplayer system Battle.net, Starcraft (1993). The main goal is to create strong game-playing arti- II players compete against each other. There are four re- ficial intelligence (AI). Such research provides information gions, each with their own ladder: Europe and Russia, North on how to model expert opponents in games, and to create and Latin America, Korea and Taiwan, and South-East Asia. AI that imitates experts. van der Werf et al. (2002) focused Games played on the ladder are ranked, and the Battle.net on predicting moves in the game of Go by observing human system automatically matches players of similar skill with expert play. Kocsis et al. (2002) used a neural network to each other. The average skill levels of the players in the predict the best moves in Go from patterns in training data four regions tend to differ, e.g., a player needs substantially consisting of expert human play. stronger skills to gain a place on the top rung of the South- In video games, research into player modeling also fo- East Asian ladder than of the European ladder. cuses on increasing the effectiveness of artificial players. In A ladder is divided into 7 leagues, which are (in order this case ‘effectiveness’ does not necessarily refer to ‘being a of increasing skill levels): bronze, silver, gold, platinum, strong player’; rather, it often concerns raising the entertain- diamond, master, and grandmaster. The bronze to platinum ment value of the game by providing the human player with leagues each contain 20% of the population of players on an opponent which is a good match for their skills (van den the ladder. The diamond level contains 18%, and the master Herik, Donkers, and Spronck 2005). Schadd, Bakkes, and level contains almost 2%. The grandmaster level consists of Spronck (2007) examined player modeling in the real-time the top 200 players of the ladder. Players always start out strategy game Spring. They were able to successfully clas- in the bronze league, and may be moved to one of the four sify the strategy of a player using hierarchical opponent bottom leagues after playing at least five placement matches.
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