Video Game AI Fundamentals

Video Game Development: Fundamental AI TechnologiesA. Babadi 1 of 55
In The Name Of God
Video Game Development
Amin Babadi
Department of Electrical and Computer Engineering
Isfahan University of Technology
Spring 2015
Fundamental AI Technologies

Outline
 What is artificial intelligence?
 Types of AI
 Structure of an AI system
 Specific technologies

Artificial Intelligence
 What is AI?

 What is AI?
o "AI is the computer simulation of intelligent behavior.“

 What is AI?
o "AI is the computer simulation of intelligent behavior.“
 What does “intelligence” mean?
o “Behavior that exhibits great ability to adapt and solve complex
problems,“ or
o "behavior that is close to that of humans"?
 Do we want artificial intelligence or artificial stupidity?!
 Game AI consists of techniques used to produce the illusion of
intelligence in the behavior of non-playable characters (NPCs).

 Take, for example, one of the classic AI problems-finding a
route from point A to point B that avoids obstacles.
 Many algorithms exist to solve this problem with varying
degrees of success.
Comparison between A* and a human path finder. Left: initial problem. Middle: human. Right: A*

 So clearly, game AI is a balance between generating behavior
that is both highly evolved and sophisticated, and behavior
that is more or less human.
 Several techniques can be used to ensure that our AIs are not
just "problem solving robots“.
 AIs must be lifelike entities that provide just the right amount
of complexity to be challenging and engaging, but not more
than that.

Structure of an AI System
 Let’s looking inside a single AI entity.
 Fundamentally, AI systems come in two flavors.
1. Virtual characters
2. Abstract controllers

1. Virtual Characters
 These are usually enemies, but can also be non-playable
characters.

2. Abstract Controllers
 Take a strategy game, for example.
 Who provides the tactical reasoning?

2. Abstract Controllers
 Take a strategy game, for example.
 Who provides the tactical reasoning?
o A strategy game needs an additional entity that acts like the master
controller of the CPU side of the battle.

 What are main elements of an AI system?

 What are main elements of an AI system?
1. A sensor or input system
2. A working memory
3. A reasoning/analysis core
4. An action/output system
 Let's briefly discuss each element of the structure.

1. Sensing the World
 All AIs need to be aware of their surroundings.
 What should be sensed and how?

1. Sensing the World
 All AIs need to be aware of their surroundings.
 What should be sensed and how?
o It largely depends on the type of game you are creating.
 let's compare the individual-level AI for a game like Quake to
the abstract controller from Age of Empires.

Quake

Quake
 In Quake, an individual enemy needs to know:
o Where is the player and where is he looking?
o What is the geometry of the surroundings?
o Sometimes, which weapons am I using and which is he using?
 In such a game, the visual system is a gross simplification of
the human one.

Age of Empires

Age of Empires
 In Age of Empires, the master controller needs to know:
o What is the balance of power in each subarea of the map?
o How much of each type of resource do I have?
o What is the breakdown of unit types: infantry, cavalry, and so on?
o What is my status in terms of the technology tree?
o What is the geometry of the game world?
 These are not simple tests at all!
 What’s the solution?

2. Memory
 Storing AI data is often complex! (Why?)

2. Memory
o The concepts being stored are not straightforward.

2. Memory
 Which one is easier? An individual AI or a master controller?

2. Memory
o An individual AI

2. Memory
o An individual AI
 How do we store more abstract information, such as the
balance of power? And how about a path?
 Some of these data structures are nontrivial, and we will often
end up with case-by-case solutions, especially when coding a
master controller.

3. Analysis/Reasoning Core
 The analysis/reasoning core is what people often think about
when they talk about AI.
 Popular methods for such tasks are finite state machines and
rule systems.
 Luckily, many games require only simple decision-making
processes.
 A lot of games praised for their great AI have been built with
relatively simple algorithms!

4. Action/Output System
 It is essential to couple our AI routines with clever action
subroutines, so we get the feeling of real intelligence.
 In fact, many games exaggerate this action system much like
in a theater play, so the character's intentions are obvious and
personality is conveyed.
 Recall the Super Mario Bros game.
o All types of crazy creatures filled the game world.
o But if you separate logic from the actual actions, you'll discover that
these AIs were all very similar.

Specific Technologies
 In the rest of this lecture, we will explore the four main AI
constructs most commonly used in game programming:
1. Finite state machines,
2. Rule systems,
3. State-space searches, and
4. Genetic algorithms.

1. Finite State Machines (FSMs)
 It is also called deterministic finite automata (DFA) or simply
state machines or automata.
 An FSM is a formalism consisting of two sets:
o A set of states that represent the scenarios or configurations the AI
can be immersed in.
o A set of transitions that are conditions that connect two states in a
directed way.
 FSMs are the most popular technique used to create game
AIs.

Example 1. A Simple FSM
 If you say something like the following, you will have created a
state machine:
o A dog is hungry.
o If you give him a bone, he will not be hungry anymore.
o He'll be quiet after eating the bone.
o And he'll become hungry after four hours of being quiet.
 Which sentences are states?
 Which sentences are transitions?

Example 2. A Soldier FSM

FSMs: Pros and Cons
 Pros:
o Intuitive to understand
o Easy to code
o Good performance
o Suitable for representing a broad range of behaviors
 Cons:
o Quick growth
o Scaling FSMs up is not always a trivial task.

2. Parallel Automata
 The core idea of parallel automata is to divide our complex
behavior into different subsystems or layers, pretending that
the entity being modeled has several brains.
 Each sub-brain is assigned a simpler automata.
 Now let’s give a gun to our soldier!
o How many brains does he/she need?

Example 3. Automata for Locomotion

Example 3. Automata Controlling the Gun

Example 3. Combined Automata

Parallel Automata: Pros and Cons
 Pros:
o More control over complex AI systems
o Great to use to model those systems where we have simultaneous but
largely independent behaviors going on
 Cons:
o Not suitable when we have interconnections

3. Synchronized FSMs
 Another addition to our FSM bag of tricks is implementing
inter-automata communications, so several of our AIs can
work together and, to a certain extent, cooperate.
 By doing so we can have enemies that attack in squads. While
one of them advances, the other provides coverage, and the
third one calls for help.
 Such an approach became very popular with the game Half-
Life, which was considered a quantum leap in action AI,
basically because of the team-based AI.

 Notice, however, that synchronizing AIs is no silver bullet.
 The method we will use is not well suited for a full-blown
strategic game with complete faction-based AIs involving
dozens of entities.
o Rule systems (explained in the next section) are much better suited
for that purpose.
 Synchronizing AIs via FSMs is just a way to increase the
expressive potential of our AI, so we can have small teams
working in parallel.

 At the core, synchronizing AIs involves a shared memory area
where all automata can write to and read data from.
 Whenever an AI wants to say something to the rest, it posts it
in the shared memory area.
 So others can browse the information; and if it is relevant to
them, they take it into consideration in their decision-making
process.

 There are two common approaches to this task:
1. One is to use a message passing architecture, which involves sending
messages to those AIs we want to synchronize to.
2. The alternative and preferred method is to use a bulletin board
architecture, where we don't send dedicated messages, but instead
post our sync messages on a shared space (the bulletin board).
 Let's make a synchronized AI for a squad of three soldiers,
designed to attack an enemy in a coordinated manner.

Example 4. Automata with no Synchronization

Example 4. Fully Synchronized Automata

4. Nondeterministic Automata
 Classic FSMs are deterministic.
o So the behavior of our AI entity will be totally predictable.
 This predictability gives the developer tight control, but is not
always desirable. (Why?)
 A limited degree of randomness can be added to the mix to
ensure that the AI will never become predictable.
 This kind of automata is usually called Nondeterministic Finite
Automata (NDFA).

Example 5. NDFA for the Soldier

5. Rule Systems (RSs)
 Finite state machines are a very convenient tool for designing
AIs.
 They can model different behaviors elegantly.
 But some phenomena are not easy to describe in terms of
states and transitions.

Example 6. Simplified Automata for a Dog
 For example, imagine the following specs for a virtual dog:
o If I'm hungry and there's a bone nearby, I'll eat it.
o If I'm hungry (but there is no bone around), I will wander.
o If I'm not hungry, but I'm sleepy, I will sleep.
o If I'm not hungry and not sleepy, I'll bark and walk.

 FSMs are well suited for behaviors that are:
o Local in nature (while we are in a certain state, only a few outcomes
are possible).
o Sequential in nature (we carry out tasks after other tasks depending
on certain conditions)
 Is the virtual dog local or not? What about sequential?
o Not local, not sequential
 All the dog actually does is act according to some priorities or
rules.
o Luckily, there is one way to model this kind of prioritized, global
behavior.

 RS allows us to model many behaviors that are tricky to model
using FSMs.
 At the core of an RS, there is a set of rules that drive our AI's
behavior.
 Each rule has the form:
Condition ⟹ Action

Example 6. A More Formal Specification
1. (Hungry) AND (Bone nearby) ⟹ Eat it
2. (Hungry) AND (No bone nearby) ⟹ Wander
3. (Not hungry) AND (Sleepy) ⟹ Sleep
4. (Not hungry) AND (Not sleepy) ⟹ Bark and walk
 The execution of an RS is really straightforward.
 We test the LHS of each expression (the conditions) in order,
and then execute the RHS (the action) of the first rule that is
activated.

 RSs, as opposed to FSMs, provide a global model of behavior.
 RSs provide a better tool when we need to model behavior
that is based on guidelines.
 We model directions as rules, placing the more important
ones closer to the top of the list, so they are priority executed.

Example 7. The Rule System for a Soldier
 Now imagine that we need to create the AI for a soldier in a
large squadron.
1. If in contact with an enemy ⟹ combat
2. If an enemy is closer than 10 meters and I'm stronger than
him ⟹ chase him
3. If an enemy is closer than 10 meters ⟹ escape him
4. If we have a command from our leader pending ⟹ execute it
5. If a friendly soldier is fighting and I have a ranged weapon
⟹ shoot at the enemy
6. Stay still

Planning and Problem Solving
 FSMs and rule sets are very useful paradigms to model simple
behavior.
 However, both approaches are limited when it comes to
modeling other, more complex behaviors.
 How can we teach a computer how to play chess?

6. State-space Search
 State-space search is a very powerful paradigm that can be
used to create strategies and plans.
 We propagate each possible move into the future and
evaluate its consequences.
 Many exploration algorithms exist for state-space problems.
o Some are blind search methods, some are heuristic search methods.

Example 8. State-space Tree for Tic-tac-toe

References
 Sanchez-Crespo’s textbook,
 Wikipedia, and
 Some other sources on the Internet.

Video Game AI Fundamentals

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

En vedette

En vedette (19)

Similaire à Video Game AI Fundamentals

Similaire à Video Game AI Fundamentals (20)

Plus de Amin Babadi

Plus de Amin Babadi (6)

Dernier

Dernier (20)

Video Game AI Fundamentals