Pathfinding in Computer Games

One of the greatest challenges in the design of realistic Artificial Intelligence (AI) in computer games is agent movement. Pathfinding strategies are usually employed as the core of any AI movement system. This report will highlight pathfinding algorithms used presently in games and their shortcomings especially when dealing with real-time pathfinding. With the advances being made in other components, such as physics engines, it is AI that is impeding the next generation of computer games. This report will focus on how machine learning techniques such as Artificial Neural Networks and Genetic Algorithms can be used to enhance an agents ability to handle pathfinding in real-time

Pedagogical Possibilities for the N-Puzzle Problem

In this paper we present work on a project funded by the National Science Foundation with a goal of unifying the Artificial Intelligence (AI) course around the theme of machine learning. Our work involves the development and testing of an adaptable framework for the presentation of core AI topics that emphasizes the relationship between AI and computer science. Several hands-on laboratory projects that can be closely integrated into an introductory AI course have been developed. We present an overview of one of the projects and describe the associated curricular materials that have been developed. The project uses machine learning as a theme to unify core AI topics in the context of the N-puzzle game. Games provide a rich framework to introduce students to search fundamentals and other core AI concepts. The paper presents several pedagogical possibilities for the N-puzzle game, the rich challenge it offers, and summarizes our experiences using it

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

An Emotion Model for Video Game AI

Modeling human behavior in computer simulations and games is a subject which draws considerable attention. Despite the increased realism of graphics in games, poor modeling of non-player characters AI often leads to a shallow and unfulfilling game experience. Recently there has been increased focus on more sophisticated AI routines which have been used in both academic and commercial games. Emotion, however, is often ignored despite being an essential element of human behavior especially under stressful conditions. Research into the use of emotion in agent-based systems seems more concerned with how to convey the emotions of agents to the human player, or how to elicit an emotional response from the human player. Only recently has there been research on modeling emotions in combat simulators. This thesis will describe an emotional model suitable for most computer games which was adapted from the DETT model and significantly expanded\u2

Challenge in Digital Games: Towards Developing a Measurement Tool

Challenge is arguably the most important experience that players seek in digital games. However, without a measure of how challenged players feel during the act of play, it is hard to design games that are neither too easy nor too hard and, therefore, truly enjoyable. Especially in industry, challenge is dominantly assessed by means of manual play testing in ad-hoc trials. The aim of this research is to create a more systematic, complete, and reliable instrument to evaluate the level of players' experienced challenge in games in the form of a questionnaire. This paper presents the key results from an extensive literature survey which will inform further development. We survey definitions of challenge, challenge types, and their relation to player experience based on the observations of game designers. We furthermore draw from empirical findings in a diverse range of fields such as game studies, human-computer interaction (HCI) and artificial intelligence (AI)