Artificial Intelligence: Genetic Algorithms
Abstract: Genetic algorithms (GAs) have great potential in game AI and they have been widely discussed in the game development community. Many attempts to apply GAs in practice have only led to frustration and disappointment, however, because many introductory texts encourage na�ve implementations of GAs that do not include the application specific enhancements that are often required in practice. This article addresses this problem by describing the roles played by the encoding scheme, genetic operators, and fitness function in a GA and describes how each of them can be designed in an application specific way to achieve maximum evolutionary performance.
Encoding Schemes and Fitness Functions for Genetic Algorithms
Abstract: Evolutionary algorithm is the broad term given to the group of optimization and search algorithms that are based on evolution and natural selection, including genetic algorithms, evolutionary computation and evolutionary strategies. Evolutionary algorithms have many advantages, in that they are robust search methods for large, complex or poorly-understood search spaces and nonlinear problems. However, they also have many disadvantages, in that they are time-consuming to develop and resource intensive when in operation. This article will introduce evolutionary algorithms, describing what they are, how they work, and how they are developed and employed, illustrated with example code. Finally, the different applications of evolutionary algorithms in games will be discussed, including examples of possible applications in different types of games.
How to Build Evolutionary Algorithms for Games
Abstract: Because most game AIs are either hared-coded or based on pre-defined scripts, players can quickly learn to anticipate how the AI will behave in certain situations. While the player will develop new strategies over time, the AI will always act as it did when the box was opened, suffering from strategic arrested development. This article describes the adaptive AI of a simple turn-based game called "Advanced Protection."
Adaptive AI: A Practical Example
This practical example of an adaptive AI displays a number of advantages over a static AI. First, the system can dynamically switch between strategies depending on the actual performance of the player - experts will be treated like experts, and novices will be treated like novices. Next, the rules and parameters of the game will be exactly the same for all strategies, which means the AI will not need to "cheat" in order to challenge expert players. Finally, the system can ensure that the AI's "best" strategies truly are the best for each individual player.
Abstract: Genetic algorithms are slowly but surely gaining popularity with game developers. Mostly as an in-house tool for tweaking NPC parameters such as ID used in the development of the bots for Quake3, but we are also beginning to see genetic algorithms used in-game, either as an integral part of the gameplay or as an aid for the user.
Building Better Genetic Algorithms
Unfortunately, many of today's programmers only know the basics of genetic algorithms, not much beyond the original paradigm devised by John Holland back in the mid sixties. This article will bring them up to date with some of the tools available to give improved performance. Techniques discussed will include various scaling techniques, speciation, fitness sharing, and other tips designed to help speedy convergence whilst retaining population diversity. In short, showing you how to get the most from your genetic algorithms.
Abstract: Genetic programming is a powerful evolutionary mechanism used to create near-optimal solutions to difficult problems. One of the major issues with traditional GP paradigms has been the relative brittleness of the organisms generated by the process: many source code organisms do not compile at all, or produce other kinds of nonsensical results. Recent advances in genetic programming, namely the grammatical evolution scheme based on such biological concepts as degenerate and cyclical DNA and gene polymorphism, promise ways to eliminate this problem and create programs that converge on a solution faster. This article explains grammatical evolution, its biological underpinnings, and a handful of other ways to refine evolutionary computing schemes, like co-evolution
Advanced Genetic Programming: New Lessons From Biology
Abstract: The purpose of this article is to introduce some newer concepts relating to the field of Genetic Algorithms (GA). GAs can introduce variability and adaptability into a game leading to non-linear gameplay and opponents who tailor their strategies to that of the player. Many limitations of mainstream GA implementations can be overcome with some simple additions. Using growth, co-evolution, speciation and other new techniques can alleviate limitations on complexity, designer bias, premature convergence and many more handicaps. These additions can reduce the disadvantages of current GAs and allow the advantages to make games much more unpredictable and challenging.
The Importance of Growth in Genetic Algorithms
Abstract: Genetic Algorithms mimic the process of natural selection to evolve solutions to problems that cannot be solved analytically. Candidate solutions, generated at random, are tested and evaluated for their fitness; the best of them are then bred and the process repeated over many generations, until an individual of satisfactory performance is found. This article explains the biological foundations of genetic algorithms and illustrates their behavior with an example: evolving a troll for a fantasy game.
Genetic Algorithms: Evolving the Perfect Troll