Game artificial intelligence
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Game artificial intelligence refers to techniques used in computer and video games to produce the illusion of intelligence in the behavior of non-player characters (NPCs). The techniques used typically draw upon existing methods from the academic field of artificial intelligence (AI). However, the term game AI is often used to refer to a broad set of algorithms that also include techniques from control theory, robotics, computer graphics and computer science in general.
Since game AI is centered on appearance of intelligence and good gameplay, its approach is very different from that of traditional AI; hacks and cheats are acceptable and, in many cases, the computer abilities must be toned down to give human players a sense of fairness. This, for example, is true in first-person shooter games, where their perfect movement and aiming is beyond human skill.
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[edit] History
The first videogames developed in the 1960s and early 1970s, like Spacewar!, Pong and Gotcha (1973), were games implemented on discrete logic and strictly based on the competition of two players, without AI.
Games that featured a single player mode with enemies started appearing in the 1970s. The first notable ones for the arcade included the 1974 Atari games Qwak (duck hunting) and Pursuit (dogfight simulator). Two text-based computer games from 1972, Hunt the Wumpus and Star Trek, also had enemies. Enemy movement was based on stored patterns. The incorporation of microprocessors would allow more computation and random elements overlaid into movement patterns.
The idea was perfected with Space Invaders (1978), sporting an increasing difficulty level, distinct movement patterns, and in-game events dependent on hash functions based on the player's input. Galaxian (1979) added more complex and varied enemy movements.
Pac-Man (1980) applied these patterns to maze games, with the added quirk of different personalities for each enemy, and Karate Champ (1984) to fighting games, although the poor AI prompted the release of a second version.
Games like Madden Football, Earl Weaver Baseball and Tony La Russa Baseball all based their AI on an attempt to duplicate on the computer the coaching or managerial style of the selected celebrity. Madden, Weaver and La Russa all did extensive work with these game development teams to maximize the accuracy of the games. Later sports titles allowed users to "tune" variables in the AI to produce a player-defined managerial or coaching strategy.
The emergence of new game genres in the 1990s prompted the use of formal AI tools like finite state machines. Real-Time Strategy games taxed the AI with many objects, incomplete information, pathfinding problems, real-time decisions and economic planning, among other things (Schwab, 2004, p.97-112). The first games of the genre had notorious problems. Herzog Zwei, for example, had almost broken pathfinding and very basic three-state state machines for unit control, and Dune II attacked the players' base in a beeline and used numerous cheats. (Schwab, 2004, p.107). Later games in the genre exhibited much better AI.
Later games have used nondeterministic AI methods, ranging from the first use of neural networks in a videogame in Battlecruiser 3000AD (1996), to the emergent behaviour and evaluation of player actions in games like Creatures or Black & White.
Far Cry (2004) exhibited very advanced AI for its time, although this made minor glitches more apparent. The enemies would react to the player's playing style and try to surround him when possible. They would also use real life military tactics to try and beat the player. The enemies did not have "cheating" AI, in the sense that they did not always know exactly where the player is all the time. They would remember his last known position and work from there.
[edit] Views
Some game programmers consider any technique that is used to help create the illusion of intelligence to be part of a game's AI. This view is controversial because it includes techniques that are also widely used outside of a game's AI engine. For example, information about potential future collisions is an important input to algorithms that help create characters that are clever enough to avoid bumping into things. But the same collision detection techniques are also commonly needed to implement a game's physics. Similarly, line of sight test results are usually important inputs to AI targeting decisions, but are also widely used inside the rendering engine. A final example is scripting, which can be a convenient tool for all aspects of game development, but is often closely associated with controlling NPCs' behavior.
Purists complain that the "AI" in the term "game AI" overstates its worth, as game AI is not about intelligence, and shares few of the objectives of the academic field of AI. Whereas "real" AI addresses fields of machine learning, decision making based on arbitrary data input, and even the ultimate goal of strong AI that can reason, "game AI" often consists of a half-dozen rules of thumb, or heuristics, that are just enough to give a good gameplay experience.
Game developers' increasing awareness of academic AI and a growing interest in computer games by the academic community is causing the definition of what counts as AI in a game to become less idiosyncratic. Nevertheless, significant differences between different application domains of AI mean that game AI can still be viewed as a distinct subfield of AI. In particular, the ability to legitimately solve some AI problems in games by cheating creates an important distinction. For example, inferring the position of an unseen object from past observations can be a difficult problem when AI is applied to robotics, but in a computer game an NPC can simply look up the position in the game's scene graph. Such cheating can lead to unrealistic behavior and so is not always desirable. But its possibility serves to distinguish game AI and leads to new problems to solve, such as when and how to use cheating.
[edit] Usage
Game AI/heuristic algorithms are used in a wide variety of quite disparate fields inside a game. The most obvious is in the control of any NPCs in the game, although scripting is currently the most common means of control. Pathfinding is another common use for AI, widely seen in real-time strategy games. Pathfinding is, as its name applies, the method for determining how to get an NPC from one point on a map to another, taking into consideration the terrain, obstacles and possibly "fog of war".
The concept of emergent AI has recently been explored in games such as Black & White and Nintendogs and toys such as Tamagotchi. The "pets" in these games are able to "learn" from actions taken by the player and their behavior is modified accordingly. While these choices are taken from a limited pool, it does often give the desired illusion of an intelligence on the other side of the screen.
[edit] Cheating AI
Cheating AI is a term used to describe the situation where the AI has bonuses over the players, e.g. giving more damage, having more hit-points, driving faster etc. It is usually used in games to artificially increase the difficulty of the game, because humans generally use more intelligent strategies than the AI, and could defeat it much more easily if it were not for the bonuses.
A common example of this is found in many racing games. If an AI opponent falls far enough behind the rest of the drivers it suddenly receives an enormous boost in speed enabling it to catch up and again become competitive. This technique is known as "rubber banding". This rubber banding in racing games sometimes also works the other way however, enabling players to catch up if they fall too far behind.
Critics claim that using this technique draws the focus away from developers to program more human-like bots. Instead they use the easy approach by letting the AI 'cheat'.
[edit] See also
[edit] References
- Bourg; Seemann (2004). AI for Game Developers. O'Reilly & Associates. ISBN 0-596-00555-5.
- Buckland (2002). AI Techniques for Game Programming. Muska & Lipman. ISBN 1-931841-08-X.
- Buckland (2004). Programming Game AI By Example. Wordware Publishing. ISBN 1-55622-078-2.
- Champandard (2003). AI Game Development. New Riders. ISBN 1-59273-004-3.
- Funge (1999). AI for Animation and Games: A Cognitive Modeling Approach. A K Peters. ISBN 1-56881-103-9.
- Funge (2004). Artificial Intelligence for Computer Games: An Introduction. A K Peters. ISBN 1-56881-208-6.
- Kruszewski, Dr. Paul (2004). A GAME-BASED COTS SYSTEM FOR SIMULATING INTELLIGENT 3D AGENTS.
- Kruszewski, Dr. Paul (2005). "AI-implant: A game-AI derived general scalable model for life-form simulation in MOUT-based applications".
- Millington (2005). Artificial Intelligence for Games. Morgan Kaufman. ISBN 0-12-497782-0.
- Schwab (2004). AI Game Engine Programming. Charles River Media. ISBN 1-58450-344-0.
- Smed and Hakonen (2006). Algorithms and Networking for Computer Games. John Wiley & Sons. ISBN 0-470-01812-7.
- Moustafa El-Arabaty(1987), Towards The Design of an Intelligent Aerospace System, AIAA
- Moustafa El-Arabaty,(1989), New Approach for the Solution of Modern Aerospace Systems Using the Artificial Intelligence, AIAA
