Developing a 5v5 Framework for DotA 2 Bot Competition Dennis Nilsson Kalle Lindqvist Malmö University Malmö University [email protected] [email protected] Faculty of Science Department of Computer Science Master Thesis in Computer Science, 15 credits 2020-05-18 Supervisors: José Maria Font Fernandez & Alberto Enrique Alvarez Uribe Examiner: Johan Holmgren CONTENTS I Introduction 3 I-A Motivation . .3 I-B Goals . .4 I-C Research Questions . .4 II Literature Review 4 II-A Data collection . .4 II-A1 Game Conferences . .4 II-B Systematic search . .4 II-B1 Keywords, search string and restrictions . .5 II-B2 Databases . .5 II-B3 Selection of papers . .5 II-C Discussion and results . .5 II-C1 Applying artificial intelligence algorithms in MOBA games . .5 II-C2 Evolving a Designer-Balanced Neural Network for Ms PacMan . .6 II-C3 An Analysis of Artificial Intelligence Techniques in Multiplayer Online Battle Arena Game Environments . .6 II-C4 MOBA: a New Arena for Game AI . .6 II-C5 MOBA games: A literature review . .7 II-C6 On The Development of Intelligent Agents For MOBA . .7 II-C7 What Contributes to Success in MOBA games? An Empirical Study Of Defense of the Ancients 2 . .7 III Research Method 7 III-A Design Science . .7 III-B Experiment . .8 III-C Threats to validity . .8 IV Frameworks 8 IV-A 1v1 framework . .8 IV-B 5v5 framework . 10 IV-B1 DotA 2 Addon . 10 IV-B2 Web API . 10 IV-B3 Client . 10 V Experiments 10 V-A Experiment 1 - Hero movement . 11 V-B Experiment 2 - Level up hero abilities . 11 V-C Experiment 3 - Hero attack . 11 V-D Experiment 4 - Buy items . 11 V-E Experiment 5 - Sell items . 11 V-F Experiment 6 - Use items . 11 V-G Experiment 7 - Target Types . 11 V-G1 Experiment 7.1 - No Target . 11 V-G2 Experiment 7.2 - Toggle . 11 V-G3 Experiment 7.3 - Target Point . 11 V-G4 Experiment 7.4 - Target Area . 11 V-G5 Experiment 7.5 - Target Unit . 11 V-G6 Experiment 7.6 - Vector Targeting . 12 V-G7 Experiment 7.7 - Combination of Target Point or Target Unit . 12 V-G8 Experiment 7.8 - Target Unit with Area of Effect . 12 V-H Experiment 8 - Validating usage of all heroes . 12 V-I Experiment 9 - Full length match . 12 1 VI Experiment results 12 VI-A Experiment 1 - Hero movement . 12 VI-B Experiment 2 - Level up hero abilities . 12 VI-C Experiment 3 - Hero attack . 13 VI-D Experiment 4 - Buy items . 13 VI-E Experiment 5 - Sell items . 13 VI-F Experiment 6 - Use items . 13 VI-G Experiment 7 - Target types . 13 VI-H Experiment 7.1 - No Target . 13 VI-I Experiment 7.2 - Toggle . 13 VI-J Experiment 7.3 - Target Point . 14 VI-K Experiment 7.4 - Target Area . 14 VI-L Experiment 7.5 - Target Unit . 14 VI-M Experiment 7.6 - Vector Targeting . 14 VI-N Experiment 7.7 - Combination of Target Point and Target Unit . 14 VI-O Experiment 7.8 - Combination of Target Unit and Target Area . 14 VI-P Experiment 8 - Full length match . 15 VI-Q Experiment 9 - Validating usage of all heroes . 15 VII Discussion 15 VII-A Future work . 15 VIII Conclusion 15 References 16 2 Abstract—Multiplayer online battle arena (MOBA) games have paths are referred to as lanes. The three lanes are called bot properties that make them suitable for research in artificial and lane, mid lane, top lane. Figure 1 gives a graphic representation computational intelligence. MOBA games are generally played of the DotA 2 map, presenting the meaning of lanes and team vs team meaning planning and organizing the team is one of the most important aspects. This makes it suitable for research towers. Mid lane is usually occupied by 1 player from each in artificial intelligence agent cooperation. team. Bot and top lane is usually occupied by 2 players from This paper presents a literature review performed in the area each team. On top of this there is also a jungle in the map of artificial and computational intelligence regarding MOBA where players can kill neutral creeps for more experience games. The findings are that there is little or no research made and gold. Players can make their own modifications to the in the area. The research found concerning MOBA games is in the context of player behaviour and toxic behaviour in the game in the programming language Lua [2]. The Lua sandbox, MOBA game league of legends. What is found is encouragement included in the workshop tools that is downloadable content, on developing frameworks supporting 5v5 matches in MOBA provides a comprehensive API to control units and view the games which this paper also presents, a 5v5 framework for bot- game state [3]. matchmaking in DotA 2 making it possible for users to develop bots to be run versus the in-game AI. Keywords—ai agent framework, DotA 2, moba, agent cooper- ation I. INTRODUCTION The topic for this thesis is research within computational intelligence and artificial intelligence. Artificial intelligence has been used to play games for a long time. As mentioned by Julian Togelius in [1], Alan Turing re-invented the Minimax algorithm to play Chess. Furthermore it sheds light on the suitability games provide for artificial intelligence research. This is due to their ability of challenging players and training the cognitive capabilities of players [1]. Different games provide different challenges. Some games challenge one player and some games challenge a team of players. Therefore the research that can be done could differ depending on which game is chosen. In this thesis a framework has been developed to provide a platform for research of artificial intelligence teamwork. The chosen game for this framework is DotA 2. Conveniently Fig. 1. Overview of the DotA 2 map. Radiant team is marked in green and a framework exists for DotA 2 but it can only handle 1v1 Dire team in red. The circles in green and red represent the teams defending matches which takes away the ability to research teamwork of towers [4] artificial intelligent agents. DotA 2 is a MOBA game where the ultimate goal is A. Motivation to destroy the opponent’s base. The opponent’s base, called Ancient, is positioned at the opposite end of the map and Ms Pac-Man [5] is a framework for competition in Ms outputs creeps that pushes the lanes during the game. Creeps Pac-Man. This is partly used for competition because of are in-game controlled AI units that are either neutral or lane the properties of the game. Research within computational creeps. Neutral creeps are found in the jungle in small camps and artificial intelligence fields is made by the help of this and provide the possibility for players to farm experience and framework. The complexity of the game is fitting to research gold. The lane creeps, spawning every 30 seconds, pushes all due to its challenges in real-time decision-making and hetero- lanes automatically towards the enemy’s Ancient. A match geneous player types. There are other AI-agent frameworks traditionally consists of two teams each having their own base used in competitions around the world such as the TAC SCM to protect and an opponent’s base to attack. Each team consists framework [6]. The idea behind these competitions is to use of five players. Each player controls their own hero i.e. a them as a testbeds for new techniques and algorithms that playable character with multiple abilities and different play can be applied, not just to game AI field, but rather the field styles. Each player is assigned a courier, used to buy and of AI research [7]. As an example, in 2017, researchers at transport items to the player, enabling the player to stay in it’s Microsoft developed an AI-agent for Ms Pac-Man that scored lane without going back to base to buy items. A hero levels the highest score ever. Rahul Mehrotra [8] who is a product up during.