Literature

• Buckland, Mat. 2005. Programming Game AI by Example
• Russell, Stuart. 2020. Artificial Intelligence: A Modern Approach (4th ed.)

Artificial Intelligence in Games

• A broad set of principles that generate behaviors, providing an illusion of intelligence
• Should be sophisticated enough to make the players think they interact with something intelligent
• Realistic AI isn't necessarily fun for players

AI Techniques

AI in games

Doom 1 (1993)

• monsters infighting (two AI characters encounter each other)
• state machines, intelligent map (SPECIAL attribute of map lines)

Age of Empires (1997)

• AI is smart enough to find and revisit other kingdoms, poor in self-defense

Half-life (1998)

• monsters can hear and track players, flee when getting defeated etc.
• soldiers had automated announcement system, ability to cover

Starcraft (1998)

• particle model for hidden units' position estimation

Unreal Tournament (1999)

• Bayesian networks

Quake III (1999)

• HFSM for decisions, AAS (Area Awareness System) for collisions and pathfinding

AI techniques in games

Black and White (2001)

• scientific-based Belief-Desire-Intention model

Halo 2 (2004)

• behavior trees and fuzzy logic

F.E.A.R. (2005)

• GOAP for tactical coordinating with squad members, suppression fire, blind fire

XCOM: Enemy Unknown (2012)

• utility-based system, measuring usefulness for every possible action

ARMA 3 (2013)

• efficient issue ordering, situation-based decisions, retreats, ambush
• HTN (Hierarchical task network) for mission generator

DOTA 2 (2013)

• OpenAI bot (since 2017) has beaten some of the greatest players

Starcraft II (2013)

• AlphaStar deep neural network, released in 2019

Challenges for Game AI

Game AI features and limits

• real-time
• limited resources
• incomplete knowledge
• planning
• learning
• acceleration (strategies)

Game AI properties

• predictability and unpredictability (surprise elements)
• support - communication between NPC and the player
• surprise - harassment, ambush, team support,...
• winning well and losing well
• cheating - acceptable as long as it doesn't get detected by the player

The AI must be fun to play against, not beat the player easily

Rules for Good Game AI

• Good AI lets the player cheat
  • e.g., combat chess system in Doom - only a handful of demons is pursuing the player
• Good AI tells the player what it is thinking
  • e.g., patrolling units saying "Someone is over there!"
• Good AI is predictable
  • e.g. mobs who are pursuing the player across the map
• Good AI can interact with the game system
  • picking up weapons, opening doors
• Good AI has its own goals
  • it doesn't just "spawn" for the sake of the player
• Good AI isn't just about enemies
  • worst example: Oblivion NPCs

Scripting

• IF-THIS-THEN-THAT
• AI behavior is completely hardcoded
• simple, easy to debug, easy to extend
• human player should behave as the developers expected
• good scripting behavior must cover a large amount of situations

   1
 // Doom 2: find player to chase
   2
 void A_Look (mobj_t* actor) {
   3
     mobj_t* targ;
   4
     actor->threshold = 0;   // any shot will wake up
   5
     targ = actor->subsector->sector->soundtarget;
   6
       if (actor->flags & MF_AMBUSH){
   7
         if (P_CheckSight (actor, actor->target))
   8
         goto seeyou;
   9
     } else goto seeyou;
  10
   
  11
     if (!P_LookForPlayers (actor, false)) return;
  12
     // go into chase state
  13
 seeyou:
  14
     P_ChasePlayer();
  15
 }

Finite State Machine

Example: Pacman FSM

Example: Pacman transition table

Example: Doomguard

Example: Doomguard

Hierarchical state machine

• also known as statecharts
• each state can have a superstate or a set of substates
• groups of states share transitions
• usually implemented as a stack
  • push a low-level state on the stack when entered
  • pop and move to the next state when finished

Fuzzy logic

• a complementary asset for state machines and scripting
• instead of thresholding, we can blurry out state transitions
• used in Halo 3 for unit control, threat assessment and classification
• other applications: proximity, mood management

Behavior Tree

• tree of hierarchical nodes that control decision making process
• originated from gaming industry since Halo 2 (2004)
• combines elements from both Scripting and HFSMs
• there is no standardized formalization
• depth-first traversal, starting with the root node
• each executed behavior passes back and returns a status
  • SUCCESS, FAILURE, RUNNING, (SUSPENDED)

Behavior Tree

Node Type	Success	Failure	Running
Selector	If one child succeeds	If all children fail	If one child is running
Sequence	If all children succeed	If one child fails	If one child is running
Decorator	It depends	It depends	It depends
Parallel	If N children succeed	If M-N children succeed	If all children are running
Action	When completed	Upon an error	During completion
Condition	If true	If false	Never

Example: Unreal Engine BT Editor

Example: Doomguard

BT Improvements

• we can define a conditional selector in order to simplify the diagram

Example: Locked door

BDI Systems

Goal-Oriented Action Planning

• centers on the idea of goals as desirable world states -> select a goal and attempt to fulfill it
• each action has a set of conditions it can satisfy, as well as a set of preconditions that must be true in order to be satisfied
• implemented for F.E.A.R (2005) and Tomb Raider (2013)

Example: F.E.A.R

• a set of goals is assigned to each mob
• these goals compete for activation, and the AI uses a planner to try to satisfy the highest priority goal
• the AI figures out the dependencies at run-time, based on the goal state and effects of actions

Soldier	Assassin	Rat
Attack	Attack	Animate
AttackCrouch	InspectDisturbance	Idle
SuppressionFire	LookAtDisturbance	GotoNode
FlushOutWithGrenade	AttackMeleeUncloaked
AttackFromCover	TraverseBlockedDoor
BlindFireFromCover	AttackFromAmbush
ReloadCovered	AttackLungeUncloaked

Predicting opponents

Occupancy map

• a grid over the game environment
• maintains probability for each grid cell
• when the opponent is not visible, the probabilities from the previous occupancy cells are propagated along the edges to neighboring cells

Threat map

• the value at any coordinate is the damage the enemy nearby can inflict
• can be easily integrated into pathfinding
• Example: combat units bypass enemy defensive structure in order to attack their infrastructure

Real-time strategy

RTS games

Dune II

1992

C&C Red Alert

1995

Total Annihilation

1997

Warcraft 3: Reign of Chaos

2002

Starcraft II

2010

Ashes of the Singularity

2016

RTS Features

Resource Control

• controlling more resources increases the players' constsruction capabilities

Tech tree

• a directed acyclic graph that contains the whole technological development of a faction

Build order (opening)

• the timing at which the first buildings are constructed

Fog of war

• fog that covers the parts of the map the player has not yet explored
• requires to scout unexplored areas to find enemy sources

Micromanagement

• way of controlling units in detail while they are in combat

Example: Starcraft 2 Tech-tree

RTS AI Layers

• micromanagement
• tactics (army positions)
• strategy (tech tree)

Layer-based AI

• each layer handles specific task
• decision components consider available information, retrieved by lower components, to make decisions; executors are triggered afterwards
• Sensor - reads the game state
• Analyzer - combines sensing data to form a coherent picture
• Memorizer - stores various types of data (terrain analysis, past decisions,...)
• Decider - a strategic decider, determines goals
• Executor - translates goals into actions
• Coordinator - synchronizes groups of units
• Actuator - executes actions by modifying the game state

Example: Layer-based AI

Example: Megaglest

• Open-source 3D RTS (LINK)
• seven factions: tech, magic, egypt, indians, norsemen, persian, romans
• rule-based AI

Megaglest architecture

Megaglest rules

Lecture Summary