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Stavros Vassos, University of Athens, Greece   stavrosv@di.uoa.gr   May 2012




INTRODUCTION TO AI
STRIPS PLANNING
.. and Applications to Video-games!
Course overview
2


       Lecture 1: Game-inspired competitions for AI research,
        AI decision making for non-player characters in games
       Lecture 2: STRIPS planning, state-space search
       Lecture 3: Planning Domain Definition Language (PDDL),
        using an award winning planner to solve Sokoban
       Lecture 4: Planning graphs, domain independent
        heuristics for STRIPS planning
       Lecture 5: Employing STRIPS planning in games:
        SimpleFPS, iThinkUnity3D, SmartWorkersRTS
       Lecture 6: Planning beyond STRIPS
Artificial Intelligence and Video Games
3


       Video Games:
         Finite State Machines
         Decision Diagrams

         Behavior Trees

         Goal Oriented Action Planning

       Academic AI on agents:
         Knowledge representation, First-order logic,
          Classical planning, Planning with preferences, …
         Belief-Desire-Intention architecture, Agent-based
          programming, …
         Probabilistic reasoning, Bayesian networks,
          Utility theory, Markov Decision Processes, …
Artificial Intelligence and Video Games
4


       Game engine:
         C++

         Creates   game-world objects with (x,y,z) coordinates and
          calculates what happens to them on every frame
         E.g., a crate is up in the air on frame1. On frame 2 the
          game engine will calculate the new position, etc
Artificial Intelligence and Video Games
5


       Game engine:
         C++

         Creates   game-world objects with (x,y,z) coordinates and
          calculates what happens to them on every frame
         E.g., a crate is up in the air on frame1. On frame 2 the
          game engine will calculate the new position, etc
         Same for non-player characters (NPCs)!
Finite State Machines (FSMs)
6


       Video Games:
         Finite State Machines
         Decision Diagrams

         Behavior Trees

         Goal Oriented Action Planning

       Academic AI on agents:
         Knowledge representation, First-order logic,
          Classical planning, Planning with preferences, …
         Belief-Desire-Intention architecture, Agent-based
          programming, …
         Probabilistic reasoning, Bayesian networks,
          Utility theory, Markov Decision Processes, …
Finite State Machines (FSMs)
7


       Recognize a formal language
Finite State Machines (FSMs)
8


       NPC behavior based on high-level states

                                                     Energy OK
                     See small enemy
        On Guard                          Fight



                                              Losing fight




                                        Run away
Finite State Machines (FSMs)
9


       Traditionally one of the first techniques for NPC
        behavior
       Very simple to understand
       Very simple to implement
         E.g.,   directly using if-then-else statements
Finite State Machines (FSMs)
10




     int NPC::think(){
       if (state==ONGUARD && seeSmallEnemy()){
         state=FIGHT;
         makeScarySound();
       }
       else if (state==FIGHT && energy>30){
         ...
       }
       else if ...
     }
Finite State Machines (FSMs)
11


        Let’s see some code from a commercial game
                             HL2-SDK, npc_BaseZombie.cpp
                             lines 1828-1870

                              switch ( m_NPCState )
                              {
                              case NPC_STATE_COMBAT:
                              …
                              case NPC_STATE_ALERT:
                              …
                              }
Finite State Machines (FSMs)
12


        Traditionally one of the first techniques for NPC
         behavior
        Very simple to understand
        Very simple to implement
          E.g.,   directly using if-then-else statements
        Separation between the work of the programmer
         and the game designers
        But also simplistic in the behaviors that can be
         expressed…
Behavior Trees (BTs)
13


        Video Games:
          Finite State Machines
          Decision Diagrams

          Behavior Trees

          Goal Oriented Action Planning

        Academic AI on agents:
          Knowledge representation, First-order logic,
           Classical planning, Planning with preferences, …
          Belief-Desire-Intention architecture, Agent-based
           programming, …
          Probabilistic reasoning, Bayesian networks,
           Utility theory, Markov Decision Processes, …
Behavior Trees (BTs)
14


        NPC behavior based on more refined conditions and
         strategies
          Tasks have a common basic structure: they are given
           CPU time to do something and return success or failure
          Leaf tasks: check a condition or execute some code

          Composite tasks: return value depend on child tasks
Behavior Trees (BTs)
15


         NPC behavior based on more refined conditions and
          strategies
           Tasks have a common basic structure: they are given
            CPU time to do something and return success or failure
           Leaf tasks: check a condition or execute some code

           Composite tasks: return value depend on child tasks

     E.g., succeed if the
     door in front of the
        NPC is open
      E.g., kick the door
     in front of the NPC
Behavior Trees (BTs)
16


        NPC behavior based on more refined conditions and
         strategies
          Tasks have a common basic structure: they are given
           CPU time to do something and return success or failure
          Leaf tasks: check a condition or execute some code

          Composite tasks: return value depend on child tasks


                                  ?
                                                E.g., succeed if any
                                                 of the child tasks
                                                       succeed
Behavior Trees (BTs)
17


        NPC behavior based on more refined conditions and
         strategies
          Tasks have a common basic structure: they are given
           CPU time to do something and return success or failure
          Leaf tasks: check a condition or execute some code

          Composite tasks: return value depend on child tasks




                                                E.g., succeed if all
                                                of the child tasks
                                                      succeed
Behavior Trees (BTs)
18


        NPC behavior based on more refined conditions
         and strategies
        Sequence task and Selector task

              →                                ?

                              ?

                                  
Behavior Trees (BTs)
19


        NPC behavior based on more refined conditions
         and strategies


             →

     Door          Move
     open?       into room
Behavior Trees (BTs)
20


        NPC behavior based on more refined conditions
         and strategies
                             ?
             →                             →

     Door          Move           Move           Move
     open?       into room       to door
                                           ?   into room
Behavior Trees (BTs)
21


        NPC behavior based on more refined conditions
         and strategies
                                  ?
             →                                    →

     Door          Move                Move              Move
     open?       into room            to door
                                                  ?    into room

                                           →          →

                          Door           Unlock       Kick         Door
                        locked?           door        door         open?
Behavior Trees (BTs)
22


       NPC behavior based on more refined conditions
     Notethat no search is involved in this paradigm: the behavior
       andtree is traversed as a kind of pre-defined program
            strategies
                                  ?
             →                                    →

     Door          Move                Move              Move
     open?       into room            to door
                                                  ?    into room

                                           →          →

                          Door           Unlock       Kick         Door
                        locked?           door        door         open?
Behavior Trees (BTs)
23


       NPC behaviorisbased ondepends on the implementation,
      Theway the tree traversed more refined conditions
       and strategies over, keep track of the current node, etc
        e.g., always start

                                  ?
             →                                    →

     Door          Move                Move              Move
     open?       into room            to door
                                                  ?    into room

                                           →          →

                          Door           Unlock       Kick         Door
                        locked?           door        door         open?
Behavior Trees (BTs)
24


        NPC behavior based on more refined conditions
         and strategies
        Non-deterministic sequence task and selector task

             ~→                                ~?

                              ~?

                                   ~
Behavior Trees (BTs)
25


        NPC behavior based on more refined conditions
         and strategies
                                  ?
             →                                    →

     Door          Move                Move               Move
     open?       into room            to door
                                                  ~?    into room

                                           →           →

                          Door           Unlock        Kick         Door
                        locked?           door         door         open?
Behavior Trees (BTs)
26


        NPC behavior based on more refined conditions
         and strategies
        Parallel sequence task (similar to sequence)

                               E.g., perform move actions
                               while also shooting at target

                                   Also used to simulate
                                 “state-like” behavior by
                              ensuring that a condition holds
Behavior Trees (BTs)
27


        NPC behavior based on more refined conditions
         and strategies
        Decorator tasks (wrap objects with same interface)




         Until
                                       Invert
         fail
Behavior Trees (BTs)
28


        NPC behavior based on more refined conditions
         and strategies
                             ?
                                             
            Until         Attack    Until          Look
            fail          enemy     fail          around


           Enemy                    Invert
           in sight

                                   Enemy
                                   in sight
Behavior Trees (BTs)
29


        One of the first commercial
         video games that used BTs
         is Halo2 (2004)
        Simple to understand
        Simple to implement
         …

        Separation between the work of the programmer
         and the game designers
        Offers the specification of fine-grained behaviors
Reactive Behavior
30


        Both FSMs and BTs are reactive techniques

          The NPC follows a pre-programmed strategy that
           specifies how the NPC should react in the game
           depending on the current state/node and conditions
           that currently hold in the game-world

         A  sequence of actions that may be executed in the
           game, e.g., [move to door, kick door, move into room],
           need to be represented explicitly in the structure of
           the FSMs or BTs
Reactive Behavior
31


        Historically, the vast amount of video games with
         NPCs use FSMs and BTs for NPC decision making

          Simpleto understand/implement
          Separation between programmers and game designers



          Any  extensions needed can be handled effectively
           using programming tricks
          The behavior is strengthened by extra information in
           the game world that is carefully prepared for NPCs
Reactive Behavior
32


        A game level from the eyes of an NPC
Reactive Behavior
33


        A game level from the eyes of an NPC
Reactive Behavior
34


        The situation today
          Open   worlds with increasing available interactions
          NPCs need to be autonomous, with their own agenda,
           goals, personality
Reactive Behavior
35


        The situation today
Reactive Behavior
36


        The situation today
          Under these circumstances, maintaining the possible and
           applicable interactions using reactive techniques
           becomes complex and difficult
          The need for more flexible techniques arises
Reactive Behavior
37


        The situation today

                               youtube link
Goal Oriented Action Planning (GOAP)
38


        Video Games:
          Finite State Machines
          Decision Diagrams

          Behavior Trees

          Goal Oriented Action Planning

        Academic AI on agents:
          Knowledge representation, First-order logic,
           Classical planning, Planning with preferences, …
          Belief-Desire-Intention architecture, Agent-based
           programming, …
          Probabilistic reasoning, Bayesian networks,
           Utility theory, Markov Decision Processes, …
Goal Oriented Action Planning (GOAP)
39


        Replace the pre-defined strategies with a
         description of goals and available actions
                                   ?
              →                                    →

     Door           Move                Move              Move
     open?        into room            to door
                                                   ?    into room

                                            →          →

                           Door           Unlock       Kick         Door
                         locked?           door        door         open?
Goal Oriented Action Planning (GOAP)
40


        Replace the pre-defined strategies with a
         description of goals and available actions




                 Move        Move            Move
               into room    to door        into room




                               Unlock     Kick
                                door      door
Goal Oriented Action Planning (GOAP)
41


        Replace the pre-defined strategies with a
         description of goals and available actions

       Move      Preconditions: Door open
     into room   Effects: In room

      Move       Preconditions: -
     to door     Effects: At door

      Unlock     Preconditions: Hold key
       door      Effects: Door open

         Kick    Preconditions: -
         door    Effects: Door open
Goal Oriented Action Planning (GOAP)
42


        Replace the pre-defined strategies with a
         description of goals and available actions

       Move      Preconditions: Door open      In room
     into room   Effects: In room

      Move       Preconditions: -
     to door     Effects: At door

      Unlock     Preconditions: Hold key
       door      Effects: Door open

         Kick    Preconditions: -
         door    Effects: Door open
Goal Oriented Action Planning (GOAP)
43


        Search in real-time for a strategy that achieves the
         goal in the current state

       Move      Preconditions: Door open      In room
     into room   Effects: In room

      Move       Preconditions: -
     to door     Effects: At door

      Unlock     Preconditions: Hold key
       door      Effects: Door open

         Kick    Preconditions: -
         door    Effects: Door open
Goal Oriented Action Planning (GOAP)
44


        Advantages
          Easy to manage a large number of generated
           behaviors
          Able to achieve different behaviors that satisfy the
           given requirements under different conditions without
           explicitly listing the resulting strategies


        But it needs to solve planning problems in a few
         frames!
Behavior Trees (BTs)
45


        One of the first commercial
         video games that used BTs
         is Halo2 (2004)
        Simple to understand
        Simple to implement
         …

        Separation between the work of the programmer
         and the game designers
        Offers the specification of fine-grained behaviors
Goal Oriented Action Planning (GOAP)
46


        One of the first commercial
         video games that used GOAP
         is FEAR (2005)
        Not so simple to understand
        Not so simple to implement
         …

        Not so clear separation between the work of the
         programmer and the game designers
        The specification of fine-grained behaviors is
         actually tricky
Goal Oriented Action Planning (GOAP)
47


        Some details about GOAP in
         FEAR:
          One  AI programmer
           responsible for NPC behavior

          Idea: Different behaviors can
           be achieved among characters
           by using GOAP and providing
           each character with same goals
           but a different set of available
           actions
Goal Oriented Action Planning (GOAP)
48
Goal Oriented Action Planning (GOAP)
49


        Simplifying STRIPS planning:
          Literalsare stored as variables
           (essentially having one argument)
          The state is stored as an array of
           a fixed size
          Search goes up to depth …3



        A* for path finding…
        A* also for planning!
Reactive Planning Vs. Classical Planning
50




        HALO2 (2004)             FEAR (2005)
        Since then BTs have      Since then GOAP has
         become a standard         not picked up much
         for NPC behavior          speed
Reactive Planning Vs. Classical Planning
51




     Behavior Trees              Goal Oriented
                                 Action Planning
Reactive Planning Vs. Classical Planning
52




     Behavior Trees                         Goal Oriented
                                            Action Planning
         A combination of these techniques?
           BTs
              for reactive decision making
           GOAP for tactical decision making
Artificial Intelligence and Video Games
53


        Amazing tools available for (indie) game developers!
Artificial Intelligence and Video Games
54


        Source available!
Bibliography
55


      Material
        Artificial
                  Intelligence for Games, Second Edition. Ian Millington,
         John Funge. Morgan Kaufmann Publishers Inc., 2009.
        Sections 5.3, 5.4

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Intro to AI STRIPS Planning & Applications in Video-games Lecture1-Part2

  • 1. Stavros Vassos, University of Athens, Greece stavrosv@di.uoa.gr May 2012 INTRODUCTION TO AI STRIPS PLANNING .. and Applications to Video-games!
  • 2. Course overview 2  Lecture 1: Game-inspired competitions for AI research, AI decision making for non-player characters in games  Lecture 2: STRIPS planning, state-space search  Lecture 3: Planning Domain Definition Language (PDDL), using an award winning planner to solve Sokoban  Lecture 4: Planning graphs, domain independent heuristics for STRIPS planning  Lecture 5: Employing STRIPS planning in games: SimpleFPS, iThinkUnity3D, SmartWorkersRTS  Lecture 6: Planning beyond STRIPS
  • 3. Artificial Intelligence and Video Games 3  Video Games:  Finite State Machines  Decision Diagrams  Behavior Trees  Goal Oriented Action Planning  Academic AI on agents:  Knowledge representation, First-order logic, Classical planning, Planning with preferences, …  Belief-Desire-Intention architecture, Agent-based programming, …  Probabilistic reasoning, Bayesian networks, Utility theory, Markov Decision Processes, …
  • 4. Artificial Intelligence and Video Games 4  Game engine:  C++  Creates game-world objects with (x,y,z) coordinates and calculates what happens to them on every frame  E.g., a crate is up in the air on frame1. On frame 2 the game engine will calculate the new position, etc
  • 5. Artificial Intelligence and Video Games 5  Game engine:  C++  Creates game-world objects with (x,y,z) coordinates and calculates what happens to them on every frame  E.g., a crate is up in the air on frame1. On frame 2 the game engine will calculate the new position, etc  Same for non-player characters (NPCs)!
  • 6. Finite State Machines (FSMs) 6  Video Games:  Finite State Machines  Decision Diagrams  Behavior Trees  Goal Oriented Action Planning  Academic AI on agents:  Knowledge representation, First-order logic, Classical planning, Planning with preferences, …  Belief-Desire-Intention architecture, Agent-based programming, …  Probabilistic reasoning, Bayesian networks, Utility theory, Markov Decision Processes, …
  • 7. Finite State Machines (FSMs) 7  Recognize a formal language
  • 8. Finite State Machines (FSMs) 8  NPC behavior based on high-level states Energy OK See small enemy On Guard Fight Losing fight Run away
  • 9. Finite State Machines (FSMs) 9  Traditionally one of the first techniques for NPC behavior  Very simple to understand  Very simple to implement  E.g., directly using if-then-else statements
  • 10. Finite State Machines (FSMs) 10 int NPC::think(){ if (state==ONGUARD && seeSmallEnemy()){ state=FIGHT; makeScarySound(); } else if (state==FIGHT && energy>30){ ... } else if ... }
  • 11. Finite State Machines (FSMs) 11  Let’s see some code from a commercial game  HL2-SDK, npc_BaseZombie.cpp  lines 1828-1870 switch ( m_NPCState ) { case NPC_STATE_COMBAT: … case NPC_STATE_ALERT: … }
  • 12. Finite State Machines (FSMs) 12  Traditionally one of the first techniques for NPC behavior  Very simple to understand  Very simple to implement  E.g., directly using if-then-else statements  Separation between the work of the programmer and the game designers  But also simplistic in the behaviors that can be expressed…
  • 13. Behavior Trees (BTs) 13  Video Games:  Finite State Machines  Decision Diagrams  Behavior Trees  Goal Oriented Action Planning  Academic AI on agents:  Knowledge representation, First-order logic, Classical planning, Planning with preferences, …  Belief-Desire-Intention architecture, Agent-based programming, …  Probabilistic reasoning, Bayesian networks, Utility theory, Markov Decision Processes, …
  • 14. Behavior Trees (BTs) 14  NPC behavior based on more refined conditions and strategies  Tasks have a common basic structure: they are given CPU time to do something and return success or failure  Leaf tasks: check a condition or execute some code  Composite tasks: return value depend on child tasks
  • 15. Behavior Trees (BTs) 15  NPC behavior based on more refined conditions and strategies  Tasks have a common basic structure: they are given CPU time to do something and return success or failure  Leaf tasks: check a condition or execute some code  Composite tasks: return value depend on child tasks E.g., succeed if the door in front of the NPC is open E.g., kick the door in front of the NPC
  • 16. Behavior Trees (BTs) 16  NPC behavior based on more refined conditions and strategies  Tasks have a common basic structure: they are given CPU time to do something and return success or failure  Leaf tasks: check a condition or execute some code  Composite tasks: return value depend on child tasks ? E.g., succeed if any of the child tasks succeed
  • 17. Behavior Trees (BTs) 17  NPC behavior based on more refined conditions and strategies  Tasks have a common basic structure: they are given CPU time to do something and return success or failure  Leaf tasks: check a condition or execute some code  Composite tasks: return value depend on child tasks E.g., succeed if all  of the child tasks succeed
  • 18. Behavior Trees (BTs) 18  NPC behavior based on more refined conditions and strategies  Sequence task and Selector task → ? ? 
  • 19. Behavior Trees (BTs) 19  NPC behavior based on more refined conditions and strategies → Door Move open? into room
  • 20. Behavior Trees (BTs) 20  NPC behavior based on more refined conditions and strategies ? → → Door Move Move Move open? into room to door ? into room
  • 21. Behavior Trees (BTs) 21  NPC behavior based on more refined conditions and strategies ? → → Door Move Move Move open? into room to door ? into room → → Door Unlock Kick Door locked? door door open?
  • 22. Behavior Trees (BTs) 22 NPC behavior based on more refined conditions Notethat no search is involved in this paradigm: the behavior andtree is traversed as a kind of pre-defined program strategies ? → → Door Move Move Move open? into room to door ? into room → → Door Unlock Kick Door locked? door door open?
  • 23. Behavior Trees (BTs) 23 NPC behaviorisbased ondepends on the implementation,  Theway the tree traversed more refined conditions and strategies over, keep track of the current node, etc e.g., always start ? → → Door Move Move Move open? into room to door ? into room → → Door Unlock Kick Door locked? door door open?
  • 24. Behavior Trees (BTs) 24  NPC behavior based on more refined conditions and strategies  Non-deterministic sequence task and selector task ~→ ~? ~? ~
  • 25. Behavior Trees (BTs) 25  NPC behavior based on more refined conditions and strategies ? → → Door Move Move Move open? into room to door ~? into room → → Door Unlock Kick Door locked? door door open?
  • 26. Behavior Trees (BTs) 26  NPC behavior based on more refined conditions and strategies  Parallel sequence task (similar to sequence)  E.g., perform move actions while also shooting at target Also used to simulate “state-like” behavior by ensuring that a condition holds
  • 27. Behavior Trees (BTs) 27  NPC behavior based on more refined conditions and strategies  Decorator tasks (wrap objects with same interface) Until Invert fail
  • 28. Behavior Trees (BTs) 28  NPC behavior based on more refined conditions and strategies ?   Until Attack Until Look fail enemy fail around Enemy Invert in sight Enemy in sight
  • 29. Behavior Trees (BTs) 29  One of the first commercial video games that used BTs is Halo2 (2004)  Simple to understand  Simple to implement …  Separation between the work of the programmer and the game designers  Offers the specification of fine-grained behaviors
  • 30. Reactive Behavior 30  Both FSMs and BTs are reactive techniques  The NPC follows a pre-programmed strategy that specifies how the NPC should react in the game depending on the current state/node and conditions that currently hold in the game-world A sequence of actions that may be executed in the game, e.g., [move to door, kick door, move into room], need to be represented explicitly in the structure of the FSMs or BTs
  • 31. Reactive Behavior 31  Historically, the vast amount of video games with NPCs use FSMs and BTs for NPC decision making  Simpleto understand/implement  Separation between programmers and game designers  Any extensions needed can be handled effectively using programming tricks  The behavior is strengthened by extra information in the game world that is carefully prepared for NPCs
  • 32. Reactive Behavior 32  A game level from the eyes of an NPC
  • 33. Reactive Behavior 33  A game level from the eyes of an NPC
  • 34. Reactive Behavior 34  The situation today  Open worlds with increasing available interactions  NPCs need to be autonomous, with their own agenda, goals, personality
  • 35. Reactive Behavior 35  The situation today
  • 36. Reactive Behavior 36  The situation today  Under these circumstances, maintaining the possible and applicable interactions using reactive techniques becomes complex and difficult  The need for more flexible techniques arises
  • 37. Reactive Behavior 37  The situation today youtube link
  • 38. Goal Oriented Action Planning (GOAP) 38  Video Games:  Finite State Machines  Decision Diagrams  Behavior Trees  Goal Oriented Action Planning  Academic AI on agents:  Knowledge representation, First-order logic, Classical planning, Planning with preferences, …  Belief-Desire-Intention architecture, Agent-based programming, …  Probabilistic reasoning, Bayesian networks, Utility theory, Markov Decision Processes, …
  • 39. Goal Oriented Action Planning (GOAP) 39  Replace the pre-defined strategies with a description of goals and available actions ? → → Door Move Move Move open? into room to door ? into room → → Door Unlock Kick Door locked? door door open?
  • 40. Goal Oriented Action Planning (GOAP) 40  Replace the pre-defined strategies with a description of goals and available actions Move Move Move into room to door into room Unlock Kick door door
  • 41. Goal Oriented Action Planning (GOAP) 41  Replace the pre-defined strategies with a description of goals and available actions Move Preconditions: Door open into room Effects: In room Move Preconditions: - to door Effects: At door Unlock Preconditions: Hold key door Effects: Door open Kick Preconditions: - door Effects: Door open
  • 42. Goal Oriented Action Planning (GOAP) 42  Replace the pre-defined strategies with a description of goals and available actions Move Preconditions: Door open In room into room Effects: In room Move Preconditions: - to door Effects: At door Unlock Preconditions: Hold key door Effects: Door open Kick Preconditions: - door Effects: Door open
  • 43. Goal Oriented Action Planning (GOAP) 43  Search in real-time for a strategy that achieves the goal in the current state Move Preconditions: Door open In room into room Effects: In room Move Preconditions: - to door Effects: At door Unlock Preconditions: Hold key door Effects: Door open Kick Preconditions: - door Effects: Door open
  • 44. Goal Oriented Action Planning (GOAP) 44  Advantages  Easy to manage a large number of generated behaviors  Able to achieve different behaviors that satisfy the given requirements under different conditions without explicitly listing the resulting strategies  But it needs to solve planning problems in a few frames!
  • 45. Behavior Trees (BTs) 45  One of the first commercial video games that used BTs is Halo2 (2004)  Simple to understand  Simple to implement …  Separation between the work of the programmer and the game designers  Offers the specification of fine-grained behaviors
  • 46. Goal Oriented Action Planning (GOAP) 46  One of the first commercial video games that used GOAP is FEAR (2005)  Not so simple to understand  Not so simple to implement …  Not so clear separation between the work of the programmer and the game designers  The specification of fine-grained behaviors is actually tricky
  • 47. Goal Oriented Action Planning (GOAP) 47  Some details about GOAP in FEAR:  One AI programmer responsible for NPC behavior  Idea: Different behaviors can be achieved among characters by using GOAP and providing each character with same goals but a different set of available actions
  • 48. Goal Oriented Action Planning (GOAP) 48
  • 49. Goal Oriented Action Planning (GOAP) 49  Simplifying STRIPS planning:  Literalsare stored as variables (essentially having one argument)  The state is stored as an array of a fixed size  Search goes up to depth …3  A* for path finding…  A* also for planning!
  • 50. Reactive Planning Vs. Classical Planning 50  HALO2 (2004)  FEAR (2005)  Since then BTs have  Since then GOAP has become a standard not picked up much for NPC behavior speed
  • 51. Reactive Planning Vs. Classical Planning 51 Behavior Trees Goal Oriented Action Planning
  • 52. Reactive Planning Vs. Classical Planning 52 Behavior Trees Goal Oriented Action Planning  A combination of these techniques?  BTs for reactive decision making  GOAP for tactical decision making
  • 53. Artificial Intelligence and Video Games 53  Amazing tools available for (indie) game developers!
  • 54. Artificial Intelligence and Video Games 54  Source available!
  • 55. Bibliography 55  Material  Artificial Intelligence for Games, Second Edition. Ian Millington, John Funge. Morgan Kaufmann Publishers Inc., 2009.  Sections 5.3, 5.4