Artificial Intelligence: Genre - Sports
Abstract: Artificial intelligence systems in games have become so complex that often one engineer cannot write the entire structure alone. Using the Basketball Artificial Intelligence Tool (BAiT) we were able to integrate the artificial intelligence for NBA 2007 based entirely upon designer data entry and manipulation. While this approach has many positives there are also some drawbacks to implementing a system like this. There are also some necessary precautions that one should take before even attempting this process.
Abstract: AI has been a sport for many decades. In this article we discuss some of the major competitions between AI game players and discuss the impact on the media and the public of success in these competitions. We discuss some of our own experiences in running AI competitions and provided pointers to running a successful competition. We consider non-programmatic ways that AI has been created, and how this might be use in a new genre of game where the player trains the AI for each player rather than controlling them directly.
Custom Tool Design for Game AI
Abstract: While presentation aspects like graphics and sound are important to a successful commercial game, it is likewise important that the gameplay, the non-presentational behaviour of the game, is engaging to the player. Considerable effort is invested in testing and re.ning gameplay throughout the development process. We present an overall view of the gameplay management problem and, more concretely, our recent research on the gameplay analysis part of this task. This consists of an active learning methodology, implemented in software tools, for largely automating the analysis of game behaviour in order to augment the abilities of game designers. The SAGA-ML (semi-automated gameplay analysis by machine learning) system is demonstrated in a real commercial context, Electronic Arts' FIFA'99 Soccer title, where it has identi.ed exploitable weaknesses in the game that allow easy scoring by players.
Semi-Automated Gameplay Analysis by Machine Learning
Abstract: Dead reckoning is a set of techniques used to calculate the motion of objects not entirely within an agent's control. This article explores the equations required to implement dead reckoning, and shows how it can apply in a variety of game contexts, for example the calculation of the optimal trajectory for a pass or a shot in a sports simulation, as well as multiple wargame problems.
Dead Reckoning in Sports and Strategy Games
Abstract: This article focuses on the sport of basketball; however, the concepts presented in the article are applicable to a wide variety of games. The goal of the article is to give the reader a solid understanding about the things to consider when designing an architecture for a sports game. The article also describes the concepts and critical components necessary to successfully design an AI system that is easy to understand, build, maintain and extend.
Building a Sports AI Architecture
The article covers, in detail, the concepts of agent plans, team management, agent AI, and touches on the critical points of agent mechanics. The architecture presented in the article serves as the foundation for Microsoft's NBA Inside Drive franchise and has been used in three shipped versions of the game.
Simulating Real Animal Behavior
Agent Cooperation in FSMs for Baseball