In this paper we propose an architecture for specifying the interaction of non-player characters (NPCs) in the game-world in a way that abstracts common tasks in four main conceptual components, namely perception, deliberation, control, action. We argue that this architecture, inspired by AI research on autonomous agents and robots, can offer a number of benefits in the form of abstraction, modularity, re-usability and higher degrees of personalization for the behavior of each NPC. We also show how this architecture can be used to tackle a simple scenario related to the navigation of NPCs under incomplete information about the obstacles that may obstruct the various way-points in the game, in a simple and effective way.
There is a variety of work that aims for action-based AI for NPCs. Traditionally, a combination of scripts and finite state machine are used in interactive games for controlling NPCs. These methods, even if fairly limited, allow the game designer to control every aspect of the NPCs actions. This approach has been employed in different types of succesfull videogames such as the Role Playing Game (RPG) or the First Person Shooter (FPS) . Scripts are written off-line in a high-level language and are used to define simple behaviors for NPCs. Procedural script generation has been proposed in @cite_5 by using simple patter templates which are tuned and combined by hand. Complex behaviors can be developed in a short amount of time but many of the intricacies of the classical scripting approach are not solved and it remains difficult to manage the NPCs as the complexity of the virtual world increases.
@cite_5
2140685838 2152729775 2027281017 2147596804
In most modern video games, character behavior is scripted; no matter how many times the player exploits a weakness, that weakness is never repaired. Yet, if game characters could learn through interacting with the player, behavior could improve as the game is played, keeping it interesting. This paper introduces the real-time Neuroevolution of Augmenting Topologies (rtNEAT) method for evolving increasingly complex artificial neural networks in real time, as a game is being played. The rtNEAT method allows agents to change and improve during the game. In fact, rtNEAT makes possible an entirely new genre of video games in which the player trains a team of agents through a series of customized exercises. To demonstrate this concept, the Neuroevolving Robotic Operatives (NERO) game was built based on rtNEAT. In NERO, the player trains a team of virtual robots for combat against other players' teams. This paper describes results from this novel application of machine learning, and demonstrates that rtNEAT makes possible video games like NERO where agents evolve and adapt in real time. In the future, rtNEAT may allow new kinds of educational and training applications through interactive and adapting games. Developing computer-controlled groups to engage in combat, control the use of limited resources, and create units and buildings in real-time strategy (RTS) games is a novel application in game AI. However, tightly controlled online commercial game pose challenges to researchers interested in observing player activities, constructing player strategy models, and developing practical AI technology in them. Instead of setting up new programming environments or building a large amount of agentpsilas decision rules by playerpsilas experience for conducting real-time AI research, the authors use replays of the commercial RTS game StarCraft to evaluate human player behaviors and to construct an intelligent system to learn human-like decisions and behaviors. A case-based reasoning approach was applied for the purpose of training our system to learn and predict player strategies. Our analysis indicates that the proposed system is capable of learning and predicting individual player strategies, and that players provide evidence of their personal characteristics through their building construction order. In massively multiplayer online role-playing games (MMORPGs), each race holds some attributes and skills. Each skill contains several abilities such as physical damage and hit rate. All those attributes and abilities are functions of the character's level, which are called Ability-Increasing Functions (AIFs). A well-balanced MMORPG is characterized by having a set of well-balanced AIFs. In this paper, we propose a coevolutionary design method, including integration with the modified probabilistic incremental program evolution (PIPE) and the cooperative coevolutionary algorithm (CCEA), to solve the balance problem of MMORPGs. Moreover, we construct a simplest turn-based game model and perform a series of experiments based on it. The results indicate that the proposed method is able to obtain a set of well-balanced AIFs more efficiently, compared with the simple genetic algorithm (SGA), the simulated annealing algorithm (SAA) and the hybrid discrete particle swarm optimization (HDPSO) algorithm. The results also show that the performance of PIPE has been significantly improved through the modification works. In most modern video games, character be- havior is scripted; no matter how many times the player exploits a weakness, that weakness is never repaired. Yet if game characters could learn through interacting with the player, behavior could improve during game- play, keeping it interesting. This paper introduces the real-time NeuroEvolution of Augmenting Topologies (rt- NEAT) method for evolving increasingly complex arti- ficial neural networks in real time, as a game is being played. The rtNEAT method allows agents to change and improve during the game. In fact, rtNEAT makes possible a new genre of video games in which the player teaches a team of agents through a series of customized training exercises. In order to demonstrate this concept in the NeuroEvolving Robotic Operatives (NERO) game, the player trains a team of robots for combat. This paper describes results from this novel application of machine learning, and also demonstrates how multiple agents can evolve and adapt in video games like NERO in real time using rtNEAT. In the future, rtNEAT may allow new kinds of educational and training applications that adapt online as the user gains new skills.