The Challenge of Believability in Video Games: Definitions, Agents Models and Imitation Learning

In this paper, we address the problem of creating believable agents (virtual characters) in video games. We consider only one meaning of believability, ``giving the feeling of being controlled by a player'', and outline the problem of its evaluation. We present several models for agents in games which can produce believable behaviours, both from industry and research. For high level of believability, learning and especially imitation learning seems to be the way to go. We make a quick overview of different approaches to make video games' agents learn from players. To conclude we propose a two-step method to develop new models for believable agents. First we must find the criteria for believability for our application and define an evaluation method. Then the model and the learning algorithm can be designed

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

Autonomous characters in virtual environments: The technologies involved in artificial life and their affects on perceived intelligence and playability of computer games

Computer games are viewed by academics as un֊grounded hack and patch experiments. "The industry lacks the formalism and requirement for a "perfect" solution often necessary in the academic world " [Woob]. Academic Artifical Intelligence (AI) is often viewed as un-implementable and narrow minded by the majority of ทon-AI programmer. "Historically, AI tended to be focused, containing detailed problems and domain-specific techniques. This focus makes for easier study - or engineering - of particular solutions. " [СһаОЗ .By implementing several well known AI techniques into the same gaming environment and judging users reactions this project aims to make links between the academic nature of AI, as well as investigate the nature of practical implementation in a gaming environment. An online Java implemented version of the 1970'ร classic Space Invaders has been developed and tested, with the Aliens being controlled by 6 different approaches to modelling AI functions. In total information from 334 individuals games was recorded. Different types of games AI can create highly varied gaming experience as highlighted by the range of values and high standard deviation values seen in the results. The link between complex behaviour, complex control systems and perceived intelligence was not supported. A positive correlation identified between how fun the users found the game and how intelligent they perceived the Aliens to be, would seem to be logical. As games get visually more and more impressive, the need for intelligent characters cannot be denied because it is one of the few way in which games can set themselves apart from the competition. Conclusions identified that computer games must remain focussed on their end- goal, that of producing a fun game. Whilst complex and clever AI can help to achieve it, the AI itself can never overshadow the end result