Evolving Tactical Behaviours for Teams of Agents in Single Player Action Games Darren Doherty Colm O’Riordan Department of Information Technology Department of Information Technology National University of Ireland, Galway National University of Ireland, Galway darren.doherty@nuigalway.ie colm.oriordan@nuigalway.ie Abstract In this paper, we describe an architecture for evolving tactics for teams of agents in single-player combative 2D games using evolutionary computing (EC) techniques. We discuss the evolutionary process adopted and the team tac- tics evolved. The individual agents in the team evolve to have different capabilities that combine together as effective tactics. We also compare the performance of the evolved team against that of a team consisting of agents incorporat- ing the built-in AI of the environment. 1 Background One of the main roles of AI in computer games is to incor- porate ‘intelligent behaviour’ in the artificial agents so as to enhance the playability of the game for the human player. The motivation behind this is to prevent the behaviour of the non-playable characters (NPCs) in the game from be- coming predictable, as occurs frequently in games that rely on scripting and finite state machines (FSMs) to describe their NPCs’ behaviour. Action games are a genre of games where conflicting groups of agents are competing in a hostile environment with the primary goal being to eliminate the opposition. One category of these games is the “shoot-em up” genre, where agents use some form of projectile weapon to attack the enemy from a distance. As tactics are highly dependant on the situation (i.e. terrain, team supplies, enemy move- ment, etc) it is very difficult for game developers to man- ually code the tactics for the NPCs. In order to imitate a tactical behaviour, a broad understanding of the situation is needed [4]. In this paper, we create an architecture to develop team tactics for a combative 2D game using genetic programming (GP) techniques. We aim to use this architecture to evolve novel and effective combat tactics that can be used by teams of enemy NPC agents in a single-player, 2D “shoot-em up” style gaming environment. We aim to develop an architec- ture that can automatically create effective team tactics for a 2D combative computer game setting. 2 Development This research builds upon previous research [2] in which a team of agents was evolved to perform as well as a de- signed team of agents. The designed team used the built- in AI of the gaming environment to define their behaviour. In the previous research, the evolving teams consisted of five agents and were evaluated by playing them against an- other team of five agents using the built-in AI of the game engine. In this research, we propose to evolve a team of five game agents against a single intelligent agent. This sin- gle agent has infinite ammunition and a health level equiv- alent to that of the team of five agents. This type of envi- ronment was chosen as it shares many similarities with the single-player “shoot-em-up” genre of games, where the sin- gle intelligent agent can be viewed as the human player in a single-player game. Thus, the tactics evolved using this environment should be effective for use by teams of enemy NPCs in single player combative computer games. As each individual team unit has only one fifth the health of the single intelligent enemy agent and much less fire- power available to it, it would be highly unlikely that five agents working in isolation would defeat the enemy agent. The five team units must therefore work together as a col- lective group and display tactical team behaviour in order to outwit and overcome the single intelligent enemy unit. 2.1 Gaming Environment The simulator is built on the 2D Raven game engine cre- ated by Matt Buckland [1]. The environment consists of an open 2-dimensional space, enclosed by four walls with an- other small wall in the center. The five agents will begin the game from the bottom center of the map facing the en- emy agent and enemy agent will start the game from the top center of the map facing the five team agents. Agents can navigate from their current position to any other position on the map by using the A* algorithm to find the shortest 1