Off-Line Evolution of Behaviour for Autonomous Agents in Real-Time Computer Games

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{anderson:ppsn2002:pp689,

• author = "Eike Falk Anderson",
• title = "Off-Line Evolution of Behaviour for Autonomous Agents in Real-Time Computer Games",
• booktitle = "Parallel Problem Solving from Nature - PPSN VII",
• address = "Granada, Spain",
• month = "7-11 " # sep,
• pages = "689--699",
• year = "2002",
• editor = "Juan J. Merelo-Guervos and Panagiotis Adamidis and Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and Hans-Paul Schwefel",
• number = "2439",
• series = "Lecture Notes in Computer Science, LNCS",
• publisher = "Springer-Verlag",
• keywords = "genetic algorithms, genetic programming, Games, Machine Learning, Fitness Evaluation",
• ISBN = "3-540-44139-5",
• DOI = "doi:10.1007/3-540-45712-7_66",
• abstract = "This paper describes and analyses a series of experiments intended to evolve a player for a variation of the classic arcade game Asteroids TM using steady state genetic programming. The player's behaviour is defined using a LISP like scripting language. While the game interprets scripts in real-time, such scripts are evolved off-line by a second program which simulates the realtime application. This method is used, as on-line evolution of the players would be too time consuming. A successful player needs to satisfy multiple conflicting objectives. This problem is addressed by the use of an automatically defined function (ADF) for each of these objectives in combination with task specific fitness functions. The overall fitness of evolved scripts is evaluated by a conventional fitness function. In addition to that, each of the ADFs is evaluated with a separate fitness function, tailored specifically to the objective that needs to be satisfied by that ADF.",

}

Genetic Programming entries for Eike Falk Anderson

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