AI in games

1
AI in games

• Simple steering, Flocking
• Production rules
• FSMs, Probabilistic FSMs
• Path Planning, Search
• Unit Movement
• Formations

2
AI in video games

• 5-10 of CPU for Realtime
• 25-50 of CPU for Turn-based
• Chase/Escape behaviors
• Group behaviors
• Finite State machines
• Adaptation/Learning

3
Questions

• How good should the AI be?
• Why are people more fun than NPCs?
• Will networked games reduce AI?
• New directions for AI in games?

4
Learning/Adaptation

• Increment aggressiveness if player is doing well
• The Computer-Based SATs do this!
• Levels are a pre-programmed version of adaptation
• Tuning
• Stability
• How might adaptation make play Better or Worse?

5
Adaptation vs. Play quality

• Do you want the monsters in Quake to get better
  as you get better?
• Force the user to live with the consequences of
  his/her actions
• Can surprise the designer (Creatures)
• Pit AI creatures against each other to find bugs
  or tune actions
• Robotwar

6
What is good AI?

• Perceived by user as challenging
• Cruel, but fair!
• User is surprised by the game
• but later understands why
• Feeling that reality will provide answers
• able to make progress solving problem
• What games have used AI effectively?

7
Chase/Evade

• Algorithm for the predator?

8
Enhancements to Chase

• Speed Control
• Velocity, Acceleration max/min
• Limited turning Radius
• Randomness
• Moves
• Patterns

9
Enhancements to Chase
Anticipation Build a model of user behavior
10
Steering Behaviors

• Pursue
• Evade
• Wander
• Obstacle Avoidance
• Wall/Path following
• Queuing
• Combine behaviors with weights
• What could go wrong?

11
Group Behaviors

• Lots of background characters to create a feeling
  of motion
• Make area appear interesting, rich, populated

12
Flocking -- (HalfLife, Unreal)
Simple version Compute trajectory to head
towards centroid

• What might go wrong?

13
Group Behaviors
Craig Reynolds SIGGRAPH 1987

• Reaction to neighbors -- Spring Forces

14
What could go wrong?
Forces balance out in dead end
Exactly aligned

• Repulsive springs around obstacles
• Does not handle changes in strategy

15
Perceptual Models
16
Production Rules

• If( enemy in sight ) fire
• If( big enemy in sight ) run away
• If( --- ) ----
• Selecting among multiple valid rules
• Priority weighting for rules or sensor events
• Random selection
• No state (in pure form)

17
Finite State Machines

• States Action to take
• Chase
• Random Walk
• Patrol
• Eat
• Transitions
• Time
• Events
• Completion of action

At woods
Chopping
Enough wood
Take Wood to closest depot
At depot
Drop wood Go back to woods
18
State Machine Problems

• Predictable
• Sometimes a good thing
• If not, use fuzzy or probabilistic state machines
• Simplistic
• Use hierarchies of FSMs (HalfLife)

19
Probabilistic State Machines

• Personalities
• Change probability that character will perform a
  given action under certain conditions

20
Probabilistic Example
Enemy Within Hand- to-Hand Range 50
Fire At Enemy
Enemy Within Hand- to-Hand Range 50
Run Away
Far Enough to Take Shot
Run out of Range
21
Probabilistic State Machines

• Other aspects
• Sight
• Memory
• Curiosity
• Fear
• Anger
• Sadness
• Sociability
• Modify probabilities on the fly?

22
Planning

• Part of intelligence is the ability to plan
• Move to a goal
• A Goal State
• Represent the world as a set of States
• Each configuration is a separate state
• Change state by applying Operators
• An Operator changes configuration from one state
  to another state

23
Path Planning

• States
• Location of Agent/NPC in space
• Discretized space
• Tiles in a tile-based game
• Floor locations in 3D
• Voxels
• Operator
• Move from one discrete location to next

24
Path Planning Algorithms

• Must Search the state space to move NPC to goal
  state
• Computational Issues
• Completeness
• Will it find an answer if one exists?
• Time complexity
• Space complexity
• Optimality
• Will it find the best solution

25
Search Strategies

• Blind search
• No domain knowledge.
• Only goal state is known
• Heuristic search
• Domain knowledge represented by heuristic rules
• Heuristics drive low-level decisions

26
Breadth-First Search

• Expand Root node
• Expand all Root nodes children
• Expand all Root nodes grandchildren
• Problem Memory size

Root
Root
Root
Child2
Child1
Child2
Child1
GChild2
GChild1
GChild3
GChild4
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Uniform Cost Search

• Modify Breadth-First by expanding cheapest nodes
  first
• Minimize g(n) cost of path so far

Root
Child2
Child1
GChild2 5
GChild3 3
GChild4 8
GChild1 9
28
Depth First Search

• Always expand the node that is deepest in the tree

Root
Root
Child1
Child1
Root
GChild2
GChild1
Child1
GChild1
29
Depth First Variants

• Depth first with cutoff C
• Dont expand a node if the path to root gt C
• Iterative Deepening
• Start the cutoff C1
• Increment the cutoff after completing all depth
  first probes for C

30
Iterative Deepening
Root
Root
Child2
Child1
Child1
Root
Root
Root
Child1
Child2
Child1
GChild1
GChild4
GChild3
GChild2
GChild1
31
Bidirectional Search

• Start 2 Trees
• Start one at start point
• Start one at goal point

32
Avoid Repeating States

• Mark states you have seen before
• In path planning
• Mark minimum distance to this node

33
Heuristic Search

• Apply approximate knowledge
• Distance measurements to goal
• Cost estimates to goal
• Use the estimate to steer exploration

34
Greedy Search

• Expand the node that yields the minimum cost
• Expand the node that is closest to target
• Depth first
• Minimize the function h(n) the heuristic cost
  function
• Not Complete!
• Local Minima

35
A Search

• Minimize sum of costs
• g(n) h(n)
• Cost so far heuristic to goal
• Guaranteed to work
• If h(n) does not overestimate cost
• Examples
• Euclidean distance

36
A Search

• Fails when there is no solution
• Avoid searching the whole space
• Do bi-directional search
• Iterative Deepening

37
Coordinated Movement

• Somewhat more difficult than moving just one NPC
• Disappearing goal
• New obstacles in path
• Collisions with other NPCs
• Groups of units
• Units in formation

38
Coordinated Elements

• Collision detection
• Detection of immediate collisions
• Near future
• Perform the usual collision detection
  optimizations
• Spatial hierarchies
• Simplified tests
• Unit approximations

39
Collision Detection

• Levels of collision
• Hard radius (small)
• Must not have 2 units overlap hard radius
• Soft radius (large)
• Soft overlap not preferred, but acceptable

40
Collision Detection

• With movement, need to avoid problems with bad
  temporal samples
• Sample frequently
• Detect collisions with extruded units
• Use a movement line
• Detect distance from Line segment

41
Unit Line

• Unit line follows path
• Can implement minimum turn radius
• Gives mechanism for position prediction
• Connected line segments
• Time stamps per segment
• Orientation per segment
• Acceleration per segment

42
Prediction line

• Given prediction, use next prediction as move
• Prediction must have dealt with collisions already

43
Collision Avoidance Planning

• Dont search a new path at each collision
• Adopt a Priority Structure
• Higher priority items move
• Lower priority items wait or get out of the way
• Case-based reasoning to perform local path
  reordering
• Pairwise comparison

44
Collision Resolution Summary

• Favor
• High priority NPCs over Low Priority
• Moving over non-moving
• Lower Priority NPCs
• Back out of the way
• Stop to allow others to pass
• General
• Resolve all high-priority collisions first

45
Avoidance

• Case 1 Both units standing
• Lower priority unit does nothing itself
• Higher unit
• Finds which unit will move
• Tells that unit to resolve hard collision by
  shortest move

46
Avoidance

• Case 2 Were not moving, other unit is moving
• Non-moving unit stays immobile

47
Avoidance

• Case 3 Were moving, other is not
• If lower priority immobile unit can get out of
  the way
• Lower unit gets out of way
• Higher unit moves past lower to get to collision
  free point
• Else If we can avoid other unit
• Avoid it!

48
Avoidance

• Case 3 Were moving, other is not
• Else Can higher unit push lower along
• Push!
• Else Recompute paths!

49
Avoidance

• Case 4 Both units moving
• Lower unit does nothing
• If hard collision inevitable and we are high unit
  
• Tell lower unit to pause
• Else If we are high unit
• Slow lower unit down and compute collision-free
  path

50
Storage

• Store predictions in a circular buffer
• If necessary, interpolate between movement steps

51
Planning

• Prediction implies
• A plan for future moves
• Once a collision has been resolved
• Record the decision that was made
• Base future movement plans on this

Blocking unit
Get-To Point
Predicted Position
52
Units, Groups, Formations

• Unit
• An individual moving NPC
• Group
• A collection of units
• Formation
• A group with position assignments per group member

53
Groups

• Groups stay together
• All units move at same speed
• All units follow the same general path
• Units arrive at the same time

Obstruction
Goal
54
Groups

• Need a hierarchical movement system
• Group structure
• Manages its own priorities
• Resolves its own collisions
• Elects a commander that traces paths, etc
• Commander can be an explicit game feature

55
Formations

• Groups with unit layouts
• Layouts designed in advance
• Additional States
• Forming
• Formed
• Broken
• Only formed formations can move

56
Formations

• Schedule arrival into position
• Start at the middle and work outwards
• Move one unit at a time into position
• Pick the next unit with
• Least collisions
• Least distance
• Formed units have highest priority
• Forming units medium priority
• Unformed units lowest

57
Formations
Not so good
1
2
3
1
4
5
7
2
9
3
6
7
8
9
5
6
4
8
1
7
2
3
5
9
6
Better
4
8
58
Formations Wheeling

• Only necessary for non-symmetric formations

Break formation here Stop motion temporarily
1
2
3
4
5
Set re-formation point here
5
4
3
2
1
59
Formations Obstacles
Scale formation layout to fit through gaps
1
2
3
4
5
1
2
3
4
5
1
2
3
4
5
Subdivide formation around small obstacles
1
2
3
4
5
60
Formations

• Adopt a hierarchy of paths to simplify
  path-planning problems
• High-level path considers only large obstacles
• Perhaps at lower resolution
• Solves problem of gross formation movement
• Paths around major terrain features

61
Formations

• Low-level path
• Detailed planning within each segment of
  high-level path
• Details of obstacle avoidance
• Implement path hierarchy with path stack

Avoidance Path
Low-Level Path1
Low-Level Path2
Low-Level Path2
High-Level Path
High-Level Path
High-Level Path
High-Level Path
62
Path Stack
Avoidance Path
Low-Level Path1
Low-Level Path2
Low-Level Path2
High-Level Path
High-Level Path
High-Level Path
High-Level Path
1
2
Low-level path
Avoidance path
63
Compound Collisions

• Solve collisions pairwise
• Start with highest priority pair
• Then, resolve the next highest priority pair
  now colliding

64
General

• Optimize for 2D if possible
• Use high-level and low-level pathing
• Units will overlap!
• Understand the update loop
• It affects unit movement
• Maintain a brief collision history

65
References

• Used with permission from CS4455 course at GA
  Tech by Chris Shaw
• http//www.cc.gatech.edu/classes/AY2005/cs4455_fal
  l/