Game AI

The history, development, strategies, and challenges of intelligence in

computer games

History and overview

• Minimax- Developed by John von Neumann in 1928- This algorithm is used extensively in game theory

• Samuel’s learning program (1959)- The program learns through the manipulation of the summation of heuristics.- If the program wins, it raises high heuristic

values and lowers low ones. If it loses, it does the opposite.

• 1960s- Progress and success in Game AI.

- Creating a successful AI meant coming up with the right rules for it to follow.

• 1970s-1980s- Transition to games as entertainment

- Using search based AI to emulate entertaining characters would be unnatural and clumsy

- Game play is based more on skill than on rules

• Early 1990s

- Increased realism becomes the primary focus of the game industry

- A rift develops between the developers of popular games and AI researchers

- Game AI that it remains little more than a modified version of Pac-man.

Examples:

First Person Shooters and Simulators (Wolfenstein 3D, Doom, Tie-Fighter): If alarmed, the AI will track and attack the player. Otherwise, it will idle.

Real Time Strategy (Warcraft, Command and Conquer): Build strongest unit (or group), scout, attack. Actual AI is stupid, map layout is used to simulate strategy.

Examples: First Person Shooters (Halflife, Deus Ex, Unreal Tournament): “Suicidal” AI is replaced by more sophisticated AI that appears to care about its own life. It coordinates attacks, calls for backup, and retreats when hurt. Friendly AI can even be given orders by the player.

Real Time Strategy: AI in the broad scheme doesn’t evolve much in RTS games. Individual unit scripts are added in to some games, but in the over all picture, the AI still simply pours units at an enemy until it is defeated. Perhaps this is because of the foreignness of the RTS paradigm, or maybe it really is the best strategy. Most successful human players abide by this “rush” strategy.

Late 1990s - To add intrigue,

developers look again to AI

• Present- Broadband and online multiplayer games- Highly immersive AI to contrast real players

Examples:Multiplayer Games (Battlefield 1942): The AI in this game is outright pitiful. While it excels at pathfinding, using tanks, planes, ships, and other implements, it is simply not human-like behavior. Among other things, the AI lacks a reasonable strategy. Instead of sending all of its troops to the front lines, it distributes them equally throughout the map. It often has trouble deciding whether it should shoot an enemy or simply lie down and stand up repeatedly.Immersive AI (Halo): Because of the plotline in this game, the friendly AI acts largely autonomously. While the human player is the “leader,” this only extends to the AI’s protection, not its obedience. This is also reflected in the opponent AI. When attacking a group of AI controlled enemies, if the leader is removed first, the remaining force becomes less effective and less organized.

Current Approaches

• Rule based – - A state machine accepts an input and produces an output.

- Simple to implement and easy to test

- Finite state machines (FSM) are familiar concepts to programmers

Good examples of the FSM approach are the early first person shooters.

pseudo-code:

while(true){

if(see_player){

chase(player);

if(distance_to_player < melee_distance){

attack(melee, player)

}else{

attack(ranged, player)

}}

- Complex FSMs become huge and unwieldy.

- Game play becomes predictable.

- Fuzzy state machines (FuSMs) introduce ambiguity

- History of states becomes relevant in decision making for FuSMs

- Creates a more realistic but still simple state machine

• Extensible AI - Possible solution to massive FSMs.

- Consists of a fully operational AI engine with a modular rule base

- Separation of the AIs persona from the game code allows the end user to modify AI behavior

- Effectively implemented in several modern games, including Unreal.

- Issues about the amount of control the end user should have- Copyright issues- Fail-safe issues

- ExAI is appropriate when a game is expected to be heavily modified aftermarket

- Currently, users can only set parameters on pre-existing AI behavior

• Learning and Behavior Development

- Instead of hard coding FSMs, let the AI learn as the player progresses

- This fits modern compute games well: AI is weak while the player is learning, and gradually strengthens.

- Mimics learning theories in modern psychology

- Accomplished through Neural Networks and Genetic Algorithms

An example of how this works is the game Black and White. In this game, the player takes the role of a deity and has a trainable creature to assist.

Problems: There is no way to tell what the creature is really learning:

“I thought it would be pretty neat to teach my creature the healing spell. Since he is always kind and generous, he ran to a village to try out his new spell. When there was no one there, the creature became upset. So he picked someone up and threw them against the mountain. The ape then healed the man and was happy.”

When a problem like this is found, even more “training” is needed to clarify the intent of the player.

Major Challenges

• Resources- Realism in computer games focused on graphics- Advanced graphics requires many CPU cycles- Recent advances in computer hardware have someone alleviated this issue.

• Deadlines- Game engine must be developed before AI can be tested- AI programmers often have to compromise to

meet deadlines

• Over Intelligence - Perfect AI would be easier to code- It would lack believability and not be fun.- Human-level AI should quit, surrender, or run

away- even fight to the end.

• Research and Development- Lack of cohesion between AI research community and game developers- AI in modern computer games seems trivial to AI researchers

• Human Level Intelligence

- Human level behavior will require AI

- We currently have only limited behavior

- Human level intelligence is really hard to create and

we still do not know how to achieve it

Applications

• Training Simulators

- Effective training often requires thousands of people

- Computer generated stand-ins can cut costs

• Virtual Environments

- Simulated worlds allows AI researchers to concentrate on algorithms instead of sensors

- Again, computer generated robots are cheaper than actual robots

• Human-level AI

- Every person is an expert on human-level intelligence

- Testing of AI is easy

• Movies - Crowd scenes

- Flocking behavior

- Realistic Movement

The future of Game AI

• Intelligent Landscape- Sims weren’t smart at all- Instead, surrounding objects contained

instructinos for use- AI finds the object that makes them happy and follows instructions

• Fuzzy AI- Rule based AI is not sufficient to model human intelligence- Uncertain algorithms such as Genetic Algorithms and Neural Networks are the future

• Immersive game space

- Instead of creating a reality, the AI borrows from the real world

- Will a game AI be the first to pass the Turing Test?

Bibliography

Peterson, Ivars. “Silicon Champions of the Game.” ScienceNewsOnline. August 2, 1997.

Wookcock, Steve. “Game AI: The State of the Industy” Game Developer Magazine. August, 1999.

“AI in Gaming” Generation5.org. http://www.generation5.org/app_game.shtml.

McCarthy, John. “Arthur Samuel: Pioneer in Machine Learning.” Stanford Computer Science Computer History Page.

Laird, John and Michael van Lent. “Human-level AI’s Killer Application: Interactive Computer Games.” American Association for Artificial Intelligence. 2000.

Laird, John. “Bridging the Gap Between Developers and Researchers.” Game Developer Magazine. August 2000.

Johnson, Steven. “Wild Things.” Wired Magazine. Issue 10.03, March 2002.