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Sports Games


Building a Sports AI Architecture
Terry Wellmann (High Voltage Software, Inc.)
AI Game Programming Wisdom 2, 2003.
Topics: General; Genres: Sports, Basketball
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.

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.

Dead Reckoning in Sports and Strategy Games
François Dominic Laramée
AI Game Programming Wisdom 2, 2003.
Topics: General; Genres: Sports, Strategy, RTS
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.

Sandbox Strategy Learning in Sports Games
Jack van Ryswyck (University of Alberta)
Game Developers Conference Proceedings, 2003.
AI Game Programming Wisdom 2 CD-ROM, 2003.
Topics: Learning; Genres: Sports
Abstract: The illusion of intelligence is most easily destroyed by predictable or static behaviour. The ability for game characters to learn and adapt to the game player's actions will become increasinly important as the AI in games develops. Yet in many games, specifically in all sports simulation games, the AI must be kept in a "sandbox": It should not potentially evolve into nonsensical territory. This lecture focuses on a strategy learning experiment as part of an AI architecture under design in collaboration with Electronic Arts for their series of sports games.

Camera AI for Replays
Sandeep V. Kharkar (Microsoft)
AI Game Programming Wisdom, 2002.
Topics: Camera AI, Replay AI; Genres: Racing, Sports
Abstract:

Agent Cooperation in FSMs for Baseball
P.J. Snavely (Acclaim Entertainment)
AI Game Programming Wisdom, 2002.
Topics: FSM, State Machines, Cooperation; Genres: Baseball, Sports
Abstract:

Intercepting a Ball
Noah Stein (Vision Scape Interactive)
AI Game Programming Wisdom, 2002.
Topics: Movement; Genres: Sports
Abstract:

Making the Play: Team Cooperation in Microsoft Baseball 3D
Steve Rabin (WizBang Software Productions)
Computer Game Developers Conference Proceedings, 1998.
Topics: Architecture, Cooperation; Genres: Sports, Baseball
Abstract: Explains a technique for coordinating 9 fielders during defensive play so that they cooperate and can dynamically cover each other's positions if needed. The technique involves using a manager entity to assign jobs by priority based on the ball and baserunner positions, instead of each player having a static job, like cover first base.

Inside NHL Powerplay 96: What Are They Thinking?
David Roberts
Computer Game Developers Conference Proceedings, 1997.
Topics: Architecture, Scripting; Genres: Sports, Hockey
Abstract:

 
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