Artificial Intelligence: Tips and Advice
Abstract: This article examines the tension in game content production between the systematic reduction of specific cases to general rules on the one hand, and the deliberate construction of unique player experiences on the other. We shall see how market and design trends are pushing games towards hybrid styles that combine these two approaches, before accusing most work in game AI of remaining too closely tied to the reduction to general rules in its commitment to strongly autonomous game agents. A quick review of related themes in sociology and psychology sets us up for the last part of the article, exploring the notion of what we call a 'situationist' game AI, capable of meeting this hybrid challenge.
Abstract: Artificial personality is a powerful conceptual framework for creating compelling artificial intelligence in most type of games. It gives direction and focus to the underlying algorithms that make up all AI, encouraging a style of play which revolves around understanding and exploiting personality archetypes such as the coward, the defender, the psycho, etc. This technique was used successfully in Bicycle� Texas Hold'em from Carbonated Games in 2006, published by MSN Games.
Artificial Personality: A Personal Approach to AI
Abstract: This article describes tips and techniques for working with designers to create better AI systems and improve their utilization in-game. It covers the advantages and disadvantages of various methods for allowing designers control over AI systems, guidelines for how much to expose in scripting systems, how to organize tunable data for data driven systems, and pitfalls to avoid in implementing such systems. It also discusses how to consider designer workflow in system design and communication tips to make sure designers understand how to use these systems.
Creating Designer Tunable AI
Abstract: This article considers the ways in which entrenched methods of game design can lead to unproductive tensions with advances in game AI technology. This issue encompasses not only methods of thinking about game design, but also styles of design documentation, and working relationships between designers and AI coders when iterating on game features. The result is not only a failure to produce useful increases in gameplay complexity. In some cases the result is actually a reduction in complexity, due to the inability of outdated design approaches to effectively control a more complex AI architecture.
Ecological Balance in AI Design
Abstract: The design of behaviors in games and massively multiplayer online games (MMOGs) is based on a style of scripting that is consistent with a cinematic perspective of game design. This style is paradigmatic of how AI is conceptualized in games. This article claims that this approach is not likely to scale in the future and calls for a more declarative style of developing and conceptualizing AI. The objective of this article is to acquaint games AI developers with thoughts and techniques that form a declarative AI design.
Abstract: As gamers demand more realistic AI and more dynamic, non-linear, and interactive game worlds, traditional methods of developing AI are beginning to show their limitations in terms of salability, robustness and general fitness for purpose. Emergence and the broader "emergent approach" to game design hold great potential as an efficient tool for avoiding these limitations by allowing high-level behaviors and flexible game environments to emerge from low level building blocks without the need for any hard-coded or scripted behaviors. Our goals in this article are to both demonstrate this case, and to explain in practical terms how emergence can be captured by the game designer.
Declarative AI Design for GamesConsiderations for MMOGs
Abstract: This article is meant to provide food for thought on a number of issues involving AI design. Creating predictable, understandable and consistent AI that doesn't beat the player all the time is no easy task. The AI programmer must make sure that the AI gives the player time to react, doesn't have cheap shots against the player and isn't too simple or too complex. The AI is meant to enrich the player's enjoyment of the game, not to frustrate them, so these rules are important to consider in order to create an enjoyable experience for the player. If you are developing a game AI the best thing you can do (besides considering these rules) is to come up with your own rules from games that you enjoy playing.
Fun Game AI Design for Beginners
Abstract: Historically, a substantial divide has existed between game AI developers and the general AI research community. Game AI developers have typically viewed academic research AI as too far removed from practical use, and academic AI researchers have remained largely uninterested in many of the common problems faced in game development. However, each group has much to gain from better communication and cooperation. While a great deal needs to be done from both sides of the divide, this article will focus on what game developers can do to better understand the academic AI research community and form better relations.
Academic AI Research and Relations with the Game Industry
Abstract: This article presents a collection of lessons learned through building and evolving an AI architecture through the development of three games for PS2, XBox, and PC: The Lord of The Rings: The Fellowship of the Ring; The Suffering; and The Suffering: Ties That Bind. The lessons cover alternate uses for A* and pathfinding, visualizations to aid AI development and debugging, benefits of a fine-grained hierarchical behavior system, and the combination of autonomy and scripted behavior for non-player characters (NPCs).
The Suffering: Game AI Lessons Learned
Abstract: This article introduces some new ideas in the field of Artificial Intelligence (AI). Many researchers are looking more toward nature for inspiration, finding many useful design solutions to the problem of behaving in a dirty, noisy world. While traditional AI techniques (OldAI) have had much success in formal domains, such as chess, they often do not scale well and are sometimes impossible to apply in less discrete domains.
New Paradigms in Artificial Intelligence
A better understanding of the techniques inspired by natural intelligence (NewAI) in addition to OldAI techniques will lead to a much more complete toolbox for an AI designer. This will allow agents to be designed to behave more naturally and a better understanding of why they fail in particular situations, leading to more believable motions and behaviors in games.
Abstract: What makes a game entertaining and fun does not necessarily correspond to making its opponent characters smarter. The player is, after all, supposed to win. However, letting a player win because of badly programmed artificial intelligence is unacceptable. Fun can be maximized when the mistakes made by computer opponents are intentional. By finely tuning opponent's mistakes, one can prevent computer opponents from looking dumb, while ensuring that the player is still capable of winning. Additionally by catching, identifying and appropriately handling genuine problems with an AI system, one can turn situations in which computer opponents would otherwise look dumb into entertainment assets. Surprisingly many game developers pay scant attention to such ideas. Developers' efforts are often so concentrated on making their computer opponents smart that they neglect to adequately address how the AI makes the game fun.
Artificial Stupidity: The Art of Intentional Mistakes
Abstract: Good game AI is tricky no write no matter what your resources are. When you're faced with limited CPU and RAM, such as with an arcade game or on a handheld, it can be nearly impossible. Arcade AI Doesn't Have to be Dumb covers various techniques used in the development of the Sega arcade game Behind Enemy Lines which helped give its AIs a bit more spontaneity and seeming intelligence than found in most shooters while not using up much memory or CPU in the process.
Arcade AI Doesn't Have to Be Dumb
The Illusion of Intelligence
Abstract: This article will talk about going back to first principles: What is the problem we are trying to solve? Why is that important - what are we really trying to do? Too early programmers settle on an answer to the first when they should more carefully examine the second. A clear example comes from the AI Roundtables at the GDC. At first the company fixated on trying to do speech input. In a world where the NPCs talk, they should be able to listen as well. This left them with the huge mountain of work to do speech recognition, and even if they could climb that mountain, the bigger mountain of natural language processing was waiting hidden behind it. After all, even if Dragon Dictate parses everything you say as well as your officemate does, it surely does not have the ability to make sense of it and respond intelligently. Instead of trying to climb these two large mountains of work, the company stepped back to the question of why it is important. It was important to give a more immersive and natural experience. So instead of doing speech, they implemented gestures. Large motion gestures are universally understood. The shrug that means "I don't know" means "I don't know" to just about everybody. They got a much better result by solving a different problem! While this article won't give people the easier problem to solve, it should help get them thinking about the process. It will also give a number of things other designers do to inspire them to "think outside the box."
Solving the Right Problem
Abstract: This article is intended to give developers who are new to Game AI a head start. It gives an overview of many techniques professional Game AI developers have found useful, that may not be immediately obvious to a novice. Topics covered include precomputing navigation, building a world with AI hints, providing fallbacks, finite-state machine organization, and data-driven approaches. Many tips reference other articles in AI Game Programming Wisdom for more detail.
12 Tips from the Trenches
The Beauty of Response Curves
Abstract: Presents 11 strategies for optimizing AI, along with tips and examples for each.
Stratagies for Optimizing AI
1. Use event-driven behavior rather than polling.
2. Reduce redundant calculations.
3. Centralize cooperation with managers.
4. Run the AI less often.
5. Distribute the processing over several frames.
6. Employ level-of-detail AI.
7. Solve only part of the problem.
8. Do the hard work offline.
9. Use emergent behavior to avoid scripting.
10. Amortize query costs with continuous bookkeeping.
11. Rethink the problem.