Games have always been popular testbeds for Artificial Intelligence (AI). In the last decade, we have seen the rise of the Multiple Online Battle Arena (MOBA) games, which are the most played games nowadays. In spite of this, there are few works that explore MOBA as a testbed for AI Research. In this paper we present and discuss the main features and opportunities offered by MOBA games to Game AI Research. We describe the various challenges faced along the game and also propose a discrete model that can be used to better understand and explore the game. With this, we aim to encourage the use of MOBA as a novel research platform for Game AI.
The first researchers were worried to present to the readers the MOBA phenomena, its basic characteristics and gameplay. In @cite_6 and @cite_13 we can find information about basic MOBA characteristics. A broad discussion of MOBA as an eSport and game design can be found in @cite_14 .
@cite_14 @cite_13 @cite_6
2182243206 1854174517 2012603127 2341380968
Despite its vast enthusiast community and influence on contemporary game designers, the MOBA (multiplayer online battle arena) remains under-explored by academics. This paper considers many meanings of “well played” reflected in the design, community, and aesthetics of the genre's most popular member, League of Legends. Originating as modifications of commercial RTS (real-time strategy) games, MOBAs present a rare study of the “rhetoric of the imaginary” in play theory applied to popular game design. The genre's reification in commercial forms such as League show how the attitudes of distributed design projects manifest themselves as values of play. A close reading of the phases in a match of League of Legends exposes one possible aesthetic framework for the consideration of eSports. Greg Costikyan's theory of uncertainty in play serves here as a backbone for the study of conventions, tension, strategy, and tactics in a team-based competitive videogame. Multiplayer Online Battle Arena (MOBA) games are among the most played digital games in the world. In these games, teams of players fight against each other in arena environments, and the gameplay is focussed on tactical combat. In this paper, we present three data-driven measures of spatio-temporal behaviour in Defence of the Ancients 2 (DotA 2): 1) Zone changes; 2) Distribution of team members and: 3) Time series clustering via a fuzzy approach. We present a method for obtaining accurate positional data from DotA 2. We investigate how behaviour varies across these measures as a function of the skill level of teams, using four tiers from novice to professional players. Results from three analyses indicate that spatio-temporal behaviour of MOBA teams is highly related to team skill. The development and professionalization of a video game requires tools for analyzing the practice of the players and teams, their tactics and strategies. These games are very popular and by nature numerical, they provide many tracks that we anal yzed in terms of team play. We studied Defense of the Ancients (DotA), a Multiplayer Online Battle Arena (MOBA), where two teams battle in a game very similar to rugby or American football. Through topological measures – area of polygon described by the players, inertia, diameter, distance to the base – that are independent of the exact nature of the game, we show that the outcome of the match can be relevantly predicted. Mining e-sport’s tracks is opening interest in further application of these tools for analyzing real time sport. Esports is computer games played in a competitive environment, and analytics in this domain is focused on player and team behavior. Multiplayer Online Battle Arena (MOBA) games are among the most played digital games in the world. In these es, teams of players fight against each other in enclosed arena environs, with a complex gameplay focused on tactical combat. Here we present a technique for segmenting matches into spatio‐temporally defined components referred to as encounters, enabling performance analysis. We apply encounter‐based analysis to match data from the popular esport game DOTA, and present win probability predictions based on encounters. Finally,metrics for evaluating team performance during match runtime are proposed.