Artificial Intelligence in Game Design

Problems and Goals

AI vs. Gaming AI

• “Standard” Artificial Intelligence• Expert Systems• Probabilistic/Fuzzy Logic• Robotics• Machine Learning

– Goal: Finding best solution to some problem– Characteristics:

• Expensive and time consuming to develop• Large number of processing cycles to run

AI vs. Gaming AI

Example: Chess (“Deep Blue”, IBM)• MINMAX algorithm • Heuristic knowledge• Databases of opening moves, endgames• Result:

– Played at world champion level (best solution)– Took several minutes per move (ok in chess)

• Not viable as commercial chess game!

Goals of Gaming AI

• Challenging but beatable:– Intelligence level artificially limited – AI not given all information

• Problem: making AI intelligent enough!– Players find and take advantage of limitations– “Cheats” compensate for bad AI

Example of Gaming AI

Player coming from unknown direction

Soldier NPC setting up ambush

What to hide behind?

Example of Gaming AI

• Choose at random?

• Current location of player?

• Base on realistic criteria– Terrain around soldier– Past player actions, etc.

This is most difficult approach!

Believable NPCs

• Opponents that offer challenge– “Orc” characters should move realistically– “Boss” characters should appear as intelligent as player

• Minions that require little micromanaging

• Other characters interesting to interact with

Believable NPCs

Intelligent Action:– Good decision making– Realistic movement – Memory of previous actions

(and possibly to improve)– Achieving goals

Believable NPCs

Believable as Characters:– Acts like human (or orc, dog, etc.)

– Has appropriate emotional states – Does not always behave predictably– Can interact with player

• Major simplification from standard AI: NPCs restricted to limited domain– Example: “Shopkeeper”

Turing Test

• Turing test for AI:

Turing Test for AI Gaming

• Does NPC act appropriately for its role in game?– Does it act “intelligently”?– Does it appear to have appropriate information?– Does it behave with the “personality” we would expect?

vs.

Game AI Structure

Movement(Action Choice)

“What actions are part of that plan?”

Example: current direction/ speed to reach next point in path

Strategy

“What are my goals?”

Example: Choosing room to move to

Tactics(Decision Making)

“How to accomplish that goal?”

Example: Choosing path to reach room

AI Engine

World Interface/Game State

Animation/Game Physics

Constraints on Gaming AI

Efficiency– Must consume few processor cycles– Must often act in real time

• Football, racing, etc.

• Simple approaches usually best– Choose fast over optimal– Tweak game to support AI– Depend on player perceptions

Tradeoffs

• Optimal solutions require complex algorithms – Shortest path O(n2) – Optimal plan Exponential tree size

• Many games use greedy algorithms– Choose action resulting in minimal “distance” to goal– O(n) time

Example of Simplification

• Pac-Man– Algorithm: Ghosts move towards player– Problem: ghosts stuck in cul-de-sacs

Example of Simplification

Black and White Game

• Creature “trained” by player by observing player actions in different situations

• Later in game creature takes same actions

• Based entirely on decision tree learningExample Allegiance Defense Tribe Attack

1 friendly weak Celtic no

2 enemy weak Celtic yes

3 friendly strong Norse no

4 enemy strong Norse no

5 friendly weak Greek no

6 enemy medium Greek yes

7 enemy strong Greek no

8 enemy medium Aztec yes

9 friendly weak Aztec no

Apparent Intelligence

NPCs can appear intelligent to player even if based on simple rules

“Theory of mind”We tend to ascribe motives/decision

making skills similar to our own to

other entities, whether this is actually

happening or not!

if hitPoints < 5 then run away from playerif distance to player < 2 units then attack playerif player visible the run towards playerelse move in random direction

Swarm Intelligence

• Simple NPCs in groups can appear to cooperate

• Decision example:if no other player shooting, I shootif in open, run for cover and shoutif in cover, reload and wait

• Orc motion example:…if NPC blocking path to player then run sidewayselse run towards player…

NPCs appear to be covering one another and coordinating attack!

Swarm Intelligence

• Give each NPC slightly different set of rules to create illusion of personalities

• Example: Pac-Manif distance to player < n then move towards playerelse wander at random

n is different for each ghost!

Large n : appeared “aggressive”

Small n : appeared “mellow”

Role of Traditional AI• Good decision making

– Acts like human (or orc, dog, etc.)– Avoids predictability

• Realistic movement – Evasion/pursuit of player– Choosing paths through complex

terrain– Cooperation among groups

• Memory of previous actions

• Achieving goals

Decision Trees

Finite State Machines

Random/Fuzzy Machines

Robotics

Swarm Intelligence

Simple Iterative Learning

Goal-based Planning