Artificial Intelligence in Computer Games

CMPE 540 Principles of Artificial Intelligence Fall 2005

CMPE 540 Principles of Artificial Intelligence

Term Paper

“Artificial Intelligence

in Computer Games”

by

Işık Barış Fidaner

Boğaziçi University

Işık Barış Fidaner 2005702532


2005



Introduction

What is game AI?

In computer games, Artificial Intelligence generally means creating

computer players that can think rationally and also can act humanly.

First problems of game AI were solved by making challenging

computer players that play the best move. But as the games involved

more imagination, new problems emerged such as designing

humanly behaving and responding characters.

Games are traditionally played by a group of players. Few

examples are chess, hide-and-seek, football. In contrast, many

computer games are single-player. So, there is a problem of

interactivity in computer games. If the player perceives the game to

be a deterministic machine, giving predictable outcomes, it probably

will no longer feel like a game. To solve this problem, AI programmers

create rational agents in the game to give the illusion of human

players. If the player is faced by the challenge to win against

intelligent rational opponents, the game feels more like a game.

First computer games were digitized versions of regular board

games in which intelligent opponents were adequate for the

simulation of the real game. But the development of computer

graphics, sounds and input technologies opened the way to new

possibilities. Games began to have stories, plots, scenes and

characters. Unlike board games, which were relatively simple

abstractions, computer games began to create a constantly

complexifying realm of fantasy worlds. But of course the deepness of

these worlds not only depended on the imaginations of game and art

designers, but also the artificial hearts and minds of the characters in



the game. Since then, game AI has been required to model more and

more intelligent and realistic behaviour.



Applications of AI in games

There are many different applications of AI in games. Many game

genres, each having different set of AI problems, create a wide

spectrum of game AI. Despite its many application possibilities, AI

algorithms and technologies advance very slowly, compared to the

technologies in other aspects of games, such as graphics or physics.

Application of game AI involves giving life to artificially intelligent

agents in games. This task is easier to directly immitate human

behaviour, because the agents are only expected to be intelligent in

the context of the game.

There are some algorithms that proved to be useful and are widely

used in certain applications of game AI. These algorithms solved

some of the AI problems in games so well that some of these

problems are not considered in the AI area anymore. Problems like

collision detection, pathfinding, visibility detection in fact do require

artificially intelligent algorithms, but they are generally given

different names such as physics or tree searching. AI is mostly related

with higher order functions of the human brain, although it contains

every level of human intelligence from perception and reasoning to

deciding and behaving.

Many game genres exist, and there are several game roles that

require AI techniques. Enemies, allies of the player, other characters

or units, any rational agent in the game uses artificial intelligence.

Game roles

The game, with its set of rules and limited world, is like a

playground for testing different kinds of artificial intelligence. These

different AI problems are practically presented as game roles. These

game roles define certain areas of problems in game AI. Well-known



AI roles are tactical enemies, partners, support characters, strategic

opponents and units (1).

Tactical enemy

Tactical enemies are most frequently used in first-person (FPS) or

third-person (TPS) shooter games. The first well-known FPS is

Wolfenstein, and the most influential one is Doom, both by id

software. In first FPS games, tactical enemies simply waited standing

until they see the player. When they did, they started firing their guns

at the player. They sometimes stopped firing and walked around.

Generally called “monsters”, these creatures neither had a memory,

nor the motive to flee to survive. They did not have an internal

representation of the map, they simply belonged to the room they

were in. Maybe their mind can be compared to that of a mosquito,

having a gun instead of a procosbis, continuously coming and

disturbing the player.

As years passed, FPS games with better graphics were developed,

but the “monster AI” seemed the same. But there was another effort

to simulate computer players, in other words “bots”, in multiplayer

FPS games. As the CPU power increased, and the players started

giving more importance to intelligent enemies, these bots'

intelligence advanced further. They became real tactical enemies,

hitting, running away, hiding and sniping. This is a result of new

algoritms applied. New bots have an internal map of the area. They

behave according to their evaluations of certain points in the map in

terms of visibility, safety and other abstract criteria (2).

Partner

Modeling partner characters require coordination with the player.

Generally, partner characters are simple. They only follow the player

as he goes, and shoot nearby enemies. This implementation only



requires intelligence for pathfinding and detecting enemies. A more

complex modeling can be allowing player to issue orders to his

partners.

Partnership can also be applied to enemies. To generalize, it is

squad / team behaviour. Team behaviour is implemented in two ways.

In the first solution, each character behaves independently, but they

also look around and if they see a teammate, they behave

accordingly. Team behaviour is a result of interactions between the

individual members (3). In the second implementation, AI thinks not

like a member, but the leader. Knowledge of members are centralized

and a global planning is made, which allows more organized tactics

(4).

Support character

Support characters in a game are similar to the minor roles in a

movie. In the adventure and role-playing games, the player generally

interacts with the support characters to follow the storyline of the

game flow. These characters have preprogrammed responses to the

questions player is allowed to ask. They do not use very sophisticated

AI, but better support characters can be modelled with more

advanced techniques. For example, non-player characters (NPC) in a

game may know about some events and tell them to each other, they

may have some attitutes towards the player or other characters in

the game (5).

Strategic opponent

Strategic opponents are the most obvious and first use of game AI.

The player is challenged by an opponent, which is in fact an

algorithm. In board games like chess, planning and predicting can

easily be used. In probabilistic games and games that the player has

more freedom, it becomes very difficult to look ahead and act



accordingly. The game being real-time, puts a time constraint and

makes the task even more difficult.

Strategic decisions are deeper than most other types of decisions.

In games with perfect information like chess, direct reasoning is

possible. But uncertainty and lack of information makes it harder to

implement an intelligently deciding agent. In many strategy games,

the programmers pick the easy way and "cheat", meaning that game

AI is given more information than what is allowed in the game. This

really makes more challenging AI but it can also annoy the player. In

a better game, the player must be able to "deceive" his opponent.

This is only possible if reasoning under uncertainty is implemented.

Computer player may "believe" in some game information, but not

know for sure. Bayesian Networks (6) or Dempster-Schafer Theory (7)

can be used for this kind of reasoning.

Unit

Units in first strategy games did not require AI. They just did

what you told them to do. But as strategy games began to involve

more complex issues such as ambushing or resource gathering, units

became simple finite state machines. Also the real-time animations

made it possible to create more lively characters.

In some "god" games, units are not player's units at all, they are

instead independent agents. For example, in Dungeon Keeper, you

did not say your creatures what to do. They just hung around,

attacked if they saw an enemy. You could change the placement of a

creature, or you could get into one and control him seeing through his

eyes. In Black & White, the player even cannot control the people, he

can only change placement of a person or give a profession to a

person. Other than that, the computer people decide what to do.

There is also the player's creature, which has more freedom, except



that the player may control it using a leash. The Sims is another

example where artificial people have "free will" in a limited scale.

Conclusion

Computer games and AI research are like two sides of one medal.

Fruits of AI research gives life to computer games, and computer

games provide new challenges for future AI researches. But of course

there is much different aspects between these two. For example,

games are industry-oriented whereas AI research is not. AI research

mainly focuses on the optimum and best solutions, whereas games

require more practical solutions.

In the future, as game characters get more and more intelligent,

games will get more interesting and more realistic. New AI tecniques

also may create new and more "intelligent" genres of games.

Everyday human relations and emotions may be used more in

gameplay. Games may be able to learn from the player, and behave

accordingly. Games may enlarge themselves by automatically

generating maps, monsters and other game elements.

Game AI is like a young tree, with many fresh branches open to

development. Leaves of the tree are wide open to the light of AI

research.



References

1. J. Laird, M. van Lent, "Human-Level AI's Killer Application:

Interactive Computer Games," Artificial Intelligence Magazine,

v.22, n.2, Summer 2001, pp. 15-25.

2. L. Liden (Valve), "Strategic and Tactical Reasoning with Waypoints",

AI Game Programming Wisdom, Ed. Steve Rabin, Charles River

Media, 2002.

3. W. Sterren (CGF-AI), "Squad Tactics: Team AI and Emergent

Maneuvers", AI Game Programming Wisdom, Ed. Steve Rabin,

Charles River Media, 2002.

4. W. Sterren (CGF-AI), "Squad Tactics: Planned Maneuvers", AI Game

Programming Wisdom, Ed. Steve Rabin, Charles River Media,

2002.

5. G. Alt, K. King (Surreal Software), "A Dynamic Reputation System

Based on Event Knowledge", AI Game Programming Wisdom, Ed.

Steve Rabin, Charles River Media, 2002.

6. G. Alt, K. King (Surreal Software), "A Dynamic Reputation System

Based on Event Knowledge", AI Game Programming Wisdom, Ed.

Steve Rabin, Charles River Media, 2002.

7. P. Tozour (Ion Storm Austin), "Introduction to Bayesian Networks

and Reasoning Under Uncertainty", AI Game Programming

Wisdom, Ed. Steve Rabin, Charles River Media, 2002.

Schaeffer, Jonathan e Herik, H. Jaap van den, 'Games, computers, and

artificial intelligence', Artificial Intelligence 134:1-2 (January 2002),

1–7.



S. Woodcock, “Game AI: The State of the Industry,” Game Developer,

Aug. 1999, pp. 34-43.

R. Evans, "The Future of AI In Games: A Personal View," Game

Developer, Aug 2001, pp. 46-49.

E. Adams, "In Defense of Academe," Game Developer, Nov 2002, pp.

55-56.


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Artificial Intelligence in Computer Games