Continuous and Reinforcement Learning Methods for First-Person Shooter Games

Machine learning is now widely studied as thebasis for artificial intelligence systems within computer games.Most existing work focuses on methods for learning staticexpert systems, typically emphasizing candidate selection. Thispaper extends this work by exploring the use of continuous andreinforcement learning techniques to develop fully-adaptivegame AI for first-person shooter bots. We begin by outlining aframework for learning static control models for tanks withinthe game BZFlag, then extend that framework using continuouslearning techniques that allow computer controlled tanks to adaptto the game style of other players, extending overall playability bythwarting attempts to infer the underlying AI. We further showhow reinforcement learning can be used to create bots that learnhow to play based solely through trial and error, providing gameengineers with a practical means to produce large numbers ofbots, each with individual intelligences and unique behaviours;all from a single initial AI model

Skilled Experience Catalogue: A Skill-Balancing Mechanism for Non-Player Characters using Reinforcement Learning

In this paper, we introduce a skill-balancing mechanism for adversarial non-player characters (NPCs), called Skilled Experience Catalogue (SEC). The objective of this mechanism is to approximately match the skill level of an NPC to an opponent in real-time. We test the technique in the context of a First-Person Shooter (FPS) game. Specifically, the technique adjusts a reinforcement learning NPC's proficiency with a weapon based on its current performance against an opponent. Firstly, a catalogue of experience, in the form of stored learning policies, is built up by playing a series of training games. Once the NPC has been sufficiently trained, the catalogue acts as a timeline of experience with incremental knowledge milestones in the form of stored learning policies. If the NPC is performing poorly, it can jump to a later stage in the learning timeline to be equipped with more informed decision-making. Likewise, if it is performing significantly better than the opponent, it will jump to an earlier stage. The NPC continues to learn in real-time using reinforcement learning but its policy is adjusted, as required, by loading the most suitable milestones for the current circumstances.Comment: IEEE Conference on Computational Intelligence and Games (CIG). August 201

Using machine learning techniques to create AI controlled players for video games

This study aims to achieve higher replay and entertainment value in a game through human-like AI behaviour in computer controlled characters called bats. In order to achieve that, an artificial intelligence system capable of learning from observation of human player play was developed. The artificial intelligence system makes use of machine learning capabilities to control the state change mechanism of the bot. The implemented system was tested by an audience of gamers and compared against bats controlled by static scripts. The data collected was focused on qualitative aspects of replay and entertainment value of the game and subjected to quantitative analysi

Artificial intelligence approaches for the generation and assessment of believable human-like behaviour in virtual characters

Having artificial agents to autonomously produce human-like behaviour is one of the most ambitious original goals of Artificial Intelligence (AI) and remains an open problem nowadays. The imitation game originally proposed by Turing constitute a very effective method to prove the indistinguishability of an artificial agent. The behaviour of an agent is said to be indistinguishable from that of a human when observers (the so-called judges in the Turing test) can not tell apart humans and non-human agents. Different environments, testing protocols, scopes and problem domains can be established to develop limited versions or variants of the original Turing test. In this paper we use a specific version of the Turing test, based on the international BotPrize competition, built in a First-Person Shooter video game, where both human players and non-player characters interact in complex virtual environments. Based on our past experience both in the BotPrize competition and other robotics and computer game AI applications we have developed three new more advanced controllers for believable agents: two based on a combination of the CERA-CRANIUM and SOAR cognitive architectures and other based on ADANN, a system for the automatic evolution and adaptation of artificial neural networks. These two new agents have been put to the test jointly with CCBot3, the winner of BotPrize 2010 competition [1], and have showed a significant improvement in the humanness ratio. Additionally, we have confronted all these bots to both First-person believability assessment (BotPrize original judging protocol) and Third-person believability assess- ment, demonstrating that the active involvement of the judge has a great impact in the recognition of human-like behaviour.MICINN -Ministerio de Ciencia e Innovación(FCT-13-7848

Improving Computer Game Bots\u27 behavior using Q-Learning

In modern computer video games, the quality of artificial characters plays a prominent role in the success of the game in the market. The aim of intelligent techniques, termed game AI, used in these games is to provide an interesting and challenging game play to a game player. Being highly sophisticated, these games present game developers with similar kind of requirements and challenges as faced by academic AI community. The game companies claim to use sophisticated game AI to model artificial characters such as computer game bots, intelligent realistic AI agents. However, these bots work via simple routines pre-programmed to suit the game map, game rules, game type, and other parameters unique to each game. Mostly, illusive intelligent behaviors are programmed using simple conditional statements and are hard-coded in the bots\u27 logic. Moreover, a game programmer has to spend considerable time configuring crisp inputs for these conditional statements. Therefore, we realize a need for machine learning techniques to dynamically improve bots\u27 behavior and save precious computer programmers\u27 man-hours. So, we selected Q-learning, a reinforcement learning technique, to evolve dynamic intelligent bots, as it is a simple, efficient, and online learning algorithm. Machine learning techniques such as reinforcement learning are know to be intractable if they use a detailed model of the world, and also requires tuning of various parameters to give satisfactory performance. Therefore, for this research we opt to examine Q-learning for evolving a few basic behaviors viz. learning to fight, and planting the bomb for computer game bots. Furthermore, we experimented on how bots would use knowledge learned from abstract models to evolve its behavior in more detailed model of the world. Bots evolved using these techniques would become more pragmatic, believable and capable of showing human-like behavior. This will provide more realistic feel to the game and provide game programmers with an efficient learning technique for programming these bots