Behavior Tree in Unreal engine 4

Huey Park
Huey ParkProgrammer at Next Floor
Behavior Tree
in Unreal Engine 4
jaewan.huey.Park@gmail.com
1
Contents
● What and Why?
● Theory
● Practice
● Q & A
● Reference
2
What?
3
What?
● Commonly used way to direct
behaviors for AI in a game.
● They can be used for any decision-
making(in systems that aren’t just
AI)
4
Why?
5
Why?
● Offer a good balance of supporting
goal-oriented(like a*) behaviors and
reactivity(like flocking).
6
Why?
● Offer a good balance of supporting
goal-oriented(like a*) behaviors and
reactivity(like flocking).
7
Why?
● Offer a good balance of supporting
goal-oriented(like a*) behaviors and
reactivity(like flocking).
8
Why not other solutions?
9
Why not use other solutions?
● There are innumerable ways to
implement AI or other decision
making systems.
● Not necessarily always best, but
they are frequently great for games.
(LOL, Uncharted 2, ...)
10
Why not use other solutions?
State Machine?
● While state machines are reasonably
intuitive for simple cases, as they become
more complex they are hard to keep goal-
oriented. As the number of states
increases, the transitions between states
become exponentially complex. Hierarchical
state machines help a little, but many of
the same issues remain.
11
10 Reasons
the Age of
Finite State Machines
is Over
12
#1 They’re Unorthodox
Problem : Building a FSM is a very
different process from any other form
of software engineering. Sure, the
concept is “designer friendly” but a
surprisingly small amount of
mainstream programming knowledge
applies to FSMs.
13
#1 They’re Unorthodox
Reason : FSMs require each state to be
wired with an explicit transition to
the next state. No programming
language requires this; everything is
done implicitly on the semantics of
the language itself.
14
#2 They’re Low-Level
Problem : The process of editing the
logic of a FSM is very low-level and
quite mechanical. You often find
rebuilding the similar behaviors over
and over from scratch - which takes a
lot of time.
15
#2 They’re Low-Level
Reason : All you can do is edit
transitions from a state to another.
There’s no way to capture higher-level
patterns that reoccur frequently like
sequences or conditionals. Meta-
programming doesn’t exist in the world
of finite state machines.
16
#3 Their Logic is Limited
Problem : Finite state machines, as
they are defined formally, are
computationally limited. This means
you can’t do things like counting by
default.
17
#3 Their Logic is Limited
Reason : If you consider events as
symbols, you can only recognize
regular grammars with a finite state
automaton. Likewise, finite state
machines can only act as transducers
for regular language.
18
#4 They Require Custom Extensions
Problem : Game developers often use
extensions to make FSMs useful in
practice. However, these hacks aren’t
always easy to understand and aren’t
so well documented either — unlike the
academic foundations of FSMs.
19
#4 They Require Custom Extensions
Reason : Because FSMs are
theoretically limited, developers must
add functionality externally to
implement certain features. This means
leveraging the underlying programming
language to implement things like
counters, timers, or any form of
memory. 20
#5 They Are Hard to Standardize
Problem : Unlike planners (HTN) or
search algorithms (A*) which are
implemented in relatively common ways,
FSMs are very difficult to reuse
across multiple games or in different
parts of the engine.
21
#5 They Are Hard to Standardize
Reason : Since FSMs aren’t turing
complete, FSMs need customizing to be
able to deal with the special cases of
each problem. This makes them very
narrowly applicable, unlike scripting
languages which can easily be
repackaged.
22
#6 They Are Not Deliberative
Problem : It takes a lot of work to
use a FSMs to create goal-directed
behaviors. This is an issue as most
purposeful AI will require dealing
with long-term goals.
23
#6 They Are Not Deliberative
Reason : FSMs operate in a reactive
mode, only dealing with events and
triggering transitions. They are not
capable of searching ahead, so you
have to edit the transitions for
dealing with all the different goals
manually.
24
#7 They Have Concurrency
Nightmares
Problem : FSMs just don’t like
concurrency. When running multiple
state machines in parallel, you either
end up with deadlocks or you have edit
them all in a way they are compatible.
25
#7 They Have Concurrency
Nightmares
Reason : FSMs have as much trouble
dealing with external resource
conflicts as they do storing
information. Solutions to make state
machines concurrent are typically
external to the FSM itself.
26
#8 They Scale Poorly
Problem : Finite state machines, even
hierarchical ones, don’t scale very
well. They often end up being edited
as a large block of logic, instead of
behaviors edited modularly.
27
#8 They Scale Poorly
Reason : FSMs are not built with the
many mechanisms of programming
languages that help them scale up to
solve large problems, in particular
indirection. Also, FSMs provide no
easy way to synchronize multiple
modular behaviors together.
28
#9 They Are Labor Intensive
Problem : It takes a lot of work to
wire up a FSM to implement any design.
Certain problems occur only because of
the state machine itself!
29
#9 They Are Labor Intensive
Reason : Dealing with all the
challenges mentioned previously takes
a lot of time by the designers, and
this ultimately becomes a source of
bugs in the behaviors.
30
#10 Industry is Moving On
Fact : Experienced game developers are
using finite state machines less and
less, switching to alternatives like
behavior trees.
31
#10 Industry is Moving On
Fact : Middleware vendors for game AI
are focusing their development efforts
mainly on planners, and 2008 should
see more of these becoming available
off the shelf.
32
Theory
33
Theory
34
Sense
ThinkAct
Theory
● Generally rely
on physics
engine
● Usually very
expensive
● Use infrequently
35
Sense
ThinkAct
Theory
● Decision Logic
● Generally quite
simple
● Design intensive
36
Sense
ThinkAct
Theory
● Action execution
● Often long
running
● Can fail to
complete
37
Sense
ThinkAct
Behavior
Tree
Theory
38
Sense
ThinkAct
Memory
Unreal Engine 4
Behavior Tree
39
Behavior
Tree
Unreal Engine 4 Behavior Tree
40
Service
DecoratorTask
Black
board
Unreal Engine 4 Behavior Tree
41
Unreal Engine 4 Behavior Tree
Root
Composite
Service
Decorator
Task
42
Unreal Engine 4 Behavior Tree
Root
● The starting execution node for
the Behavior Tree.
● Every Behavior Tree has one.
● You cannot attach Decorators or
Services to it.
43
Unreal Engine 4 Behavior Tree
Composite
● These are nodes that define the
root of a branch and define the
base rules for how that branch is
executed.
● Sequence, Selector, Simple
Parallel
44
Unreal Engine 4 Behavior Tree
Composite : Sequence
45
Unreal Engine 4 Behavior Tree
Composite : Sequence
● Sequence Node execute their
children from left to right, and
will stop executing its children
when one of their children Fails.
If a child fails, then the
Sequence fails.
46
Unreal Engine 4 Behavior Tree
Composite : Selector
47
Unreal Engine 4 Behavior Tree
Composite : Selector
● Selector Nodes execute their
children from left to right, and
will stop executing its children
when one of their children
Succeeds. If a Selector’s child
succeed, the Selector succeeds.
48
Unreal Engine 4 Behavior Tree
Composite : Simple Parallel
49
Unreal Engine 4 Behavior Tree
Composite : Simple Parallel
● The Simple Parallel node allows a
single main task node to be
executed along side of a full
tree. When the main task finishes,
the setting in Finish Mode
dictates the secondary tree.
50
Unreal Engine 4 Behavior Tree
Service
51
Unreal Engine 4 Behavior Tree
Service
● These attach to Composite nodes,
and will execute at their defined
frequency. These are often used to
make checks and to update the
Blackboard. These take the place
of traditional Parallel nodes.
52
Unreal Engine 4 Behavior Tree
Decorator
53
Unreal Engine 4 Behavior Tree
Decorator
● Also known as conditionals. These
attach to another node and make
decisions on whether or not a
branch in the tree, or even single
node, can be executed.
54
Unreal Engine 4 Behavior Tree
Task
55
Unreal Engine 4 Behavior Tree
Task
● Theres are leaves of the tree, the
nodes that “do” things.
56
Unreal Engine 4 Behavior Tree
Blackboard
57
Unreal Engine 4 Behavior Tree
Blackboard
● A blackboard is a simple place
where data can be written and read
for decision making purposes.
● A blackboard can be used by a
single AI pawn, shared by squad.
58
Unreal Engine 4 Behavior Tree
Blackboard : Why use?
● To make efficient event-driven
behaviors
● To cache calculations
● As a scratch-pad for behaviors
● To centralize data
59
Unreal Engine 4 Behavior Tree
Blackboard : Notice
● Don’t clutter the blackboard with
lots of super-specific-case data.
● If only one node needs to know
something, it can potentially
fetch the value itself rather than
adding every bit of tree.
60
Unreal Engine 4 Behavior Tree
Blackboard : Notice
● If you fail to copy data to the
blackboard properly, it may cause
you some debugging nightmare!
● If very frequently updated and
copied to the blackboard, that
could be bad for performance.
61
Practice
62
63
Airplane Dogfighting
State
● When I track
● When I get tracking
● When I can’t find target
● When I suddenly encountered an
obstacle
64
Airplane Dogfighting
Task
● Move to target
● Evasion maneuver
● Cobra maneuver
● Emergency evasion
● Throttle control
65
Airplane Dogfighting
Service
● Search airplane
● Emergency evasion check
66
Airplane Dogfighting
Decorator
● Blackboard Based Condition
● Time Limit
● Emergency Evasion Check
● In View
67
68
69
Airplane Dogfighting
https://github.com/HueyPark/Open-Fire
70
Q & A
71
References
● AiGameDev.com
● Behavior Trees What and Why : Unreal Engine
Forum
● 10 Reasons the Age of Finite State Machines
is Over
● Blackboard Documentation : Unreal Engine
Forum
● zoombapup : AI Tutorial youtube playlist
● 길안에서 묻다 : 네이버 블로그
72
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Behavior Tree in Unreal engine 4

  • 1. Behavior Tree in Unreal Engine 4 jaewan.huey.Park@gmail.com 1
  • 2. Contents ● What and Why? ● Theory ● Practice ● Q & A ● Reference 2
  • 4. What? ● Commonly used way to direct behaviors for AI in a game. ● They can be used for any decision- making(in systems that aren’t just AI) 4
  • 6. Why? ● Offer a good balance of supporting goal-oriented(like a*) behaviors and reactivity(like flocking). 6
  • 7. Why? ● Offer a good balance of supporting goal-oriented(like a*) behaviors and reactivity(like flocking). 7
  • 8. Why? ● Offer a good balance of supporting goal-oriented(like a*) behaviors and reactivity(like flocking). 8
  • 9. Why not other solutions? 9
  • 10. Why not use other solutions? ● There are innumerable ways to implement AI or other decision making systems. ● Not necessarily always best, but they are frequently great for games. (LOL, Uncharted 2, ...) 10
  • 11. Why not use other solutions? State Machine? ● While state machines are reasonably intuitive for simple cases, as they become more complex they are hard to keep goal- oriented. As the number of states increases, the transitions between states become exponentially complex. Hierarchical state machines help a little, but many of the same issues remain. 11
  • 12. 10 Reasons the Age of Finite State Machines is Over 12
  • 13. #1 They’re Unorthodox Problem : Building a FSM is a very different process from any other form of software engineering. Sure, the concept is “designer friendly” but a surprisingly small amount of mainstream programming knowledge applies to FSMs. 13
  • 14. #1 They’re Unorthodox Reason : FSMs require each state to be wired with an explicit transition to the next state. No programming language requires this; everything is done implicitly on the semantics of the language itself. 14
  • 15. #2 They’re Low-Level Problem : The process of editing the logic of a FSM is very low-level and quite mechanical. You often find rebuilding the similar behaviors over and over from scratch - which takes a lot of time. 15
  • 16. #2 They’re Low-Level Reason : All you can do is edit transitions from a state to another. There’s no way to capture higher-level patterns that reoccur frequently like sequences or conditionals. Meta- programming doesn’t exist in the world of finite state machines. 16
  • 17. #3 Their Logic is Limited Problem : Finite state machines, as they are defined formally, are computationally limited. This means you can’t do things like counting by default. 17
  • 18. #3 Their Logic is Limited Reason : If you consider events as symbols, you can only recognize regular grammars with a finite state automaton. Likewise, finite state machines can only act as transducers for regular language. 18
  • 19. #4 They Require Custom Extensions Problem : Game developers often use extensions to make FSMs useful in practice. However, these hacks aren’t always easy to understand and aren’t so well documented either — unlike the academic foundations of FSMs. 19
  • 20. #4 They Require Custom Extensions Reason : Because FSMs are theoretically limited, developers must add functionality externally to implement certain features. This means leveraging the underlying programming language to implement things like counters, timers, or any form of memory. 20
  • 21. #5 They Are Hard to Standardize Problem : Unlike planners (HTN) or search algorithms (A*) which are implemented in relatively common ways, FSMs are very difficult to reuse across multiple games or in different parts of the engine. 21
  • 22. #5 They Are Hard to Standardize Reason : Since FSMs aren’t turing complete, FSMs need customizing to be able to deal with the special cases of each problem. This makes them very narrowly applicable, unlike scripting languages which can easily be repackaged. 22
  • 23. #6 They Are Not Deliberative Problem : It takes a lot of work to use a FSMs to create goal-directed behaviors. This is an issue as most purposeful AI will require dealing with long-term goals. 23
  • 24. #6 They Are Not Deliberative Reason : FSMs operate in a reactive mode, only dealing with events and triggering transitions. They are not capable of searching ahead, so you have to edit the transitions for dealing with all the different goals manually. 24
  • 25. #7 They Have Concurrency Nightmares Problem : FSMs just don’t like concurrency. When running multiple state machines in parallel, you either end up with deadlocks or you have edit them all in a way they are compatible. 25
  • 26. #7 They Have Concurrency Nightmares Reason : FSMs have as much trouble dealing with external resource conflicts as they do storing information. Solutions to make state machines concurrent are typically external to the FSM itself. 26
  • 27. #8 They Scale Poorly Problem : Finite state machines, even hierarchical ones, don’t scale very well. They often end up being edited as a large block of logic, instead of behaviors edited modularly. 27
  • 28. #8 They Scale Poorly Reason : FSMs are not built with the many mechanisms of programming languages that help them scale up to solve large problems, in particular indirection. Also, FSMs provide no easy way to synchronize multiple modular behaviors together. 28
  • 29. #9 They Are Labor Intensive Problem : It takes a lot of work to wire up a FSM to implement any design. Certain problems occur only because of the state machine itself! 29
  • 30. #9 They Are Labor Intensive Reason : Dealing with all the challenges mentioned previously takes a lot of time by the designers, and this ultimately becomes a source of bugs in the behaviors. 30
  • 31. #10 Industry is Moving On Fact : Experienced game developers are using finite state machines less and less, switching to alternatives like behavior trees. 31
  • 32. #10 Industry is Moving On Fact : Middleware vendors for game AI are focusing their development efforts mainly on planners, and 2008 should see more of these becoming available off the shelf. 32
  • 35. Theory ● Generally rely on physics engine ● Usually very expensive ● Use infrequently 35 Sense ThinkAct
  • 36. Theory ● Decision Logic ● Generally quite simple ● Design intensive 36 Sense ThinkAct
  • 37. Theory ● Action execution ● Often long running ● Can fail to complete 37 Sense ThinkAct
  • 40. Behavior Tree Unreal Engine 4 Behavior Tree 40 Service DecoratorTask Black board
  • 41. Unreal Engine 4 Behavior Tree 41
  • 42. Unreal Engine 4 Behavior Tree Root Composite Service Decorator Task 42
  • 43. Unreal Engine 4 Behavior Tree Root ● The starting execution node for the Behavior Tree. ● Every Behavior Tree has one. ● You cannot attach Decorators or Services to it. 43
  • 44. Unreal Engine 4 Behavior Tree Composite ● These are nodes that define the root of a branch and define the base rules for how that branch is executed. ● Sequence, Selector, Simple Parallel 44
  • 45. Unreal Engine 4 Behavior Tree Composite : Sequence 45
  • 46. Unreal Engine 4 Behavior Tree Composite : Sequence ● Sequence Node execute their children from left to right, and will stop executing its children when one of their children Fails. If a child fails, then the Sequence fails. 46
  • 47. Unreal Engine 4 Behavior Tree Composite : Selector 47
  • 48. Unreal Engine 4 Behavior Tree Composite : Selector ● Selector Nodes execute their children from left to right, and will stop executing its children when one of their children Succeeds. If a Selector’s child succeed, the Selector succeeds. 48
  • 49. Unreal Engine 4 Behavior Tree Composite : Simple Parallel 49
  • 50. Unreal Engine 4 Behavior Tree Composite : Simple Parallel ● The Simple Parallel node allows a single main task node to be executed along side of a full tree. When the main task finishes, the setting in Finish Mode dictates the secondary tree. 50
  • 51. Unreal Engine 4 Behavior Tree Service 51
  • 52. Unreal Engine 4 Behavior Tree Service ● These attach to Composite nodes, and will execute at their defined frequency. These are often used to make checks and to update the Blackboard. These take the place of traditional Parallel nodes. 52
  • 53. Unreal Engine 4 Behavior Tree Decorator 53
  • 54. Unreal Engine 4 Behavior Tree Decorator ● Also known as conditionals. These attach to another node and make decisions on whether or not a branch in the tree, or even single node, can be executed. 54
  • 55. Unreal Engine 4 Behavior Tree Task 55
  • 56. Unreal Engine 4 Behavior Tree Task ● Theres are leaves of the tree, the nodes that “do” things. 56
  • 57. Unreal Engine 4 Behavior Tree Blackboard 57
  • 58. Unreal Engine 4 Behavior Tree Blackboard ● A blackboard is a simple place where data can be written and read for decision making purposes. ● A blackboard can be used by a single AI pawn, shared by squad. 58
  • 59. Unreal Engine 4 Behavior Tree Blackboard : Why use? ● To make efficient event-driven behaviors ● To cache calculations ● As a scratch-pad for behaviors ● To centralize data 59
  • 60. Unreal Engine 4 Behavior Tree Blackboard : Notice ● Don’t clutter the blackboard with lots of super-specific-case data. ● If only one node needs to know something, it can potentially fetch the value itself rather than adding every bit of tree. 60
  • 61. Unreal Engine 4 Behavior Tree Blackboard : Notice ● If you fail to copy data to the blackboard properly, it may cause you some debugging nightmare! ● If very frequently updated and copied to the blackboard, that could be bad for performance. 61
  • 63. 63
  • 64. Airplane Dogfighting State ● When I track ● When I get tracking ● When I can’t find target ● When I suddenly encountered an obstacle 64
  • 65. Airplane Dogfighting Task ● Move to target ● Evasion maneuver ● Cobra maneuver ● Emergency evasion ● Throttle control 65
  • 66. Airplane Dogfighting Service ● Search airplane ● Emergency evasion check 66
  • 67. Airplane Dogfighting Decorator ● Blackboard Based Condition ● Time Limit ● Emergency Evasion Check ● In View 67
  • 68. 68
  • 69. 69
  • 72. References ● AiGameDev.com ● Behavior Trees What and Why : Unreal Engine Forum ● 10 Reasons the Age of Finite State Machines is Over ● Blackboard Documentation : Unreal Engine Forum ● zoombapup : AI Tutorial youtube playlist ● 길안에서 묻다 : 네이버 블로그 72