Game AI - Automated Planning

Artificial Intelligence

Lecture 04 – Automated Planning

Edirlei Soares de Lima

<edirlei.lima@universidadeeuropeia.pt>

Game AI – Model

•

Pathfinding

Steering behaviours

Finite state machines

Automated planning

Behaviour trees

Randomness

Sensor systems

Machine learning

Decision Making

•

In game AI, decision making is the ability

of a character/agent to decide what to

do.

The agent processes a set of information

that it uses to generate an action that it

wants to carry out.

–

Input: agent’s knowledge about the world;

–

Output: an action request;

Decision Making

•

The knowledge can be broken down into external and

internal knowledge.

–

External knowledge: information about the game environment (e.g.

characters’ positions, level layout, noise direction).

–

Internal knowledge: information about the character’s internal state

(e.g. health, goals, last actions).

Goal-Oriented Behavior

•

So far we have focused on reactive agents: a set of inputs is

provided to the character, and an appropriate action is

selected.

–

Goal-oriented behavior is an alternative approach. It adds character

goals/desires to the decision making process.

To allow an NPC to properly anticipate the effects and take

advantage of sequences of actions, a planning process is

required.

–

Automated Planning Techniques.

Automated Planning

•

Planning is the task of finding a sequence of actions (a plan) to

achieve a goal.

Example:

–

Goal: have(sword)Λ at(castle)

–

Plan: go(dungeon), kill(enemy), get(key), go(forest),

open(chest, key), get(sword), go(castle).

•

Plan-based agent process:

1

2

3

) Formulate a goal;

) Find a plan;

) Execute the plan;

Automated Planning

•

A planning problem is usually represented through a planning

language, such as the PDDL (Planning Domain Definition

Language).

–

PDDL was derived from the original STRIPS model, which is slightly

more restrictive.

Planning problem elements:

–

Initial State;

Actions (with preconditions and effects);

Goal;

Planning Problem

•

Each state is represented as a conjunction of predicates.

–

Example: At(Truck1, Melbourne)∧ At(Truck2, Sydney).

–

Closed-world assumption: any predicates that are not mentioned are

false.

Actions are described by a set of action schemas with

parameters, preconditions, and effects.

–

Example:

Action(

Fly(p, f, t),

PRECOND: At(p, f) ∧ Plane(p) ∧ Airport(f) ∧ Airport(t)

EFFECT: ¬At(p, f) ∧ At(p, t)

)

Planning Problem

•

The precondition defines the states in which the action can be

executed.

Example:

Action(

Fly(p, f, t),

PRECOND: At(p, f) ∧ Plane(p) ∧ Airport(f) ∧ Airport(t)

EFFECT: ¬At(p, f) ∧ At(p, t)

)

–

Initial State: At(C1, SFO) ∧ At(C2, JFK) ∧ At(P1, SFO) ∧ At(P2, JFK) ∧ Cargo(C1) ∧

Cargo(C2) ∧ Plane(P1) ∧ Plane(P2) ∧ Airport (JFK) ∧ Airport (SFO)

The Fly action can be instantiated as Fly(P1, SFO, JFK) or as Fly(P2, JFK, SFO).

Planning Problem

•

The effect defines the result of executing the action.

Example:

Action(

Fly(p, f, t),

PRECOND: At(p, f) ∧ Plane(p) ∧ Airport(f) ∧ Airport(t)

EFFECT: ¬At(p, f) ∧ At(p, t)

)

–

Initial State: At(C1, SFO) ∧ At(C2, JFK) ∧ At(P1, SFO) ∧ At(P2, JFK) ∧ Cargo(C1) ∧

Cargo(C2) ∧ Plane(P1) ∧ Plane(P2) ∧ Airport (JFK) ∧ Airport (SFO)

–

Negative predicates are removed from the resulting state (e.g. ¬At(p, f));

Positive predicates are added to the resulting state (e.g. At(p, t));

Example – Air Cargo Transport

Init(At(C1, SFO) ∧ At(C2, JFK) ∧ At(P1, SFO) ∧ At(P2, JFK) ∧

Cargo(C1) ∧ Cargo(C2) ∧ Plane(P1) ∧ Plane(P2) ∧

Airport (JFK) ∧ Airport (SFO))

Goal(At(C1, JFK) ∧ At(C2, SFO))

Action(

Load(c, p, a),

PRECOND: At(c, a) ∧ At(p, a) ∧ Cargo(c) ∧ Plane(p) ∧ Airport(a)

EFFECT: ¬At(c, a) ∧ In(c, p)

)

Action(

Unload(c, p, a),

PRECOND: In(c, p) ∧ At(p, a) ∧ Cargo(c) ∧ Plane(p) ∧ Airport(a)

EFFECT: At(c, a) ∧ ¬In(c, p)

)

Action(

Fly(p, f, t),

PRECOND: At(p, f) ∧ Plane(p) ∧ Airport(f) ∧ Airport(t)

EFFECT: ¬At(p, f) ∧ At(p, t)

)

Example – Blocks World

Init(On(A, Table) ∧ On(B, Table) ∧ On(C, A) ∧

Block(A) ∧ Block(B) ∧ Block(C) ∧ Clear(B) ∧

Clear(C))

Goal(On(A,B) ∧ On(B,C))

Action(

Move(b, x, y),

PRECOND: On(b, x) ∧ Clear(b) ∧ Clear(y) ∧

Block(b) ∧ Block(y) ∧ (b ≠ x) ∧

(

b ≠ y) ∧ (x ≠ y),

EFFECT: On(b, y) ∧ Clear(x) ∧ ¬On(b, x) ∧

Clear(y)

¬

)

Action(

MoveToTable(b, x),

PRECOND: On(b, x) ∧ Clear(b) ∧ Block(b) ∧

(b ≠ x),

EFFECT: On(b, Table) ∧ Clear(x) ∧ ¬On(b, x)

)

Planning Algorithms

•

The description of a planning problem defines a search

problem: we can search from the initial state looking for a goal.

Planning approaches:

–

Progressive: forward state-space search;

–

Regressive: backward relevant-states search;

Forward State-Space Search

take c3

…

take c2

move r1

…

Backward Relevant-States Search

^g1

a₁

^a4

g₄

g₂

g₀

a₂

a₃

s₀

^a5

g₅

g₃

Planning Domain Definition Language

•

A planning problem is usually represented through a planning

language, such as the PDDL (Planning Domain Definition

Language).

–

PDDL was derived from the original STRIPS model, which is slightly

more restrictive.

Planning problems specified in PDDL are defined in two files:

–

Domain File: types, predicates, and actions.

–

Problem File: objects, initial state, and goal.

PDDL – Syntax

•

Domain File:

(define (domain <domain name>)

(

<

[

<

:requirements :strips :equality :typing)

:types <list of types>)

:constants <list of constants>)

PDDL code for predicates>

PDDL code for first action>

...]

PDDL code for last action>

)

•

Problem File:

(define (problem <problem name>)

(

<

:domain <domain name>)

PDDL code for objects>

PDDL code for initial state>

PDDL code for goal specification>

)

PDDL – Example Problem

•

“There is robot that can move between two rooms

and pickup/putdown boxes with two arms. Initially,

the robot and 4 boxes are at room 1. The robot must

take all boxes to room 2.”

Room 1

Room 2

PDDL – Domain File

•

Types:

(:types room box arm)

Constants:

(:constants left right - arm)

Predicates:

–

robot-at(x) – true if the robot is at room x;

box-at(x, y) – true if the box x is at room y;

free(x) – true if the arm x is not holding a box;

carry(x, y) – true if the arm x is holding a box y;

(:predicates

(

robot-at ?x - room)

box-at ?x - box ?y - room)

free ?x - arm)

carry ?x – box ?y - arm)

)

PDDL – Domain File

•

Action: move the robot from room x to room y.

Precondition: robot-at(x) must be true.

Effect: robot-at(y) becomes true and robot-at(x) becomes

false.

(:action move

:

parameters (?x ?y - room)

precondition (robot-at ?x)

effect (and (robot-at ?y) (not (robot-at ?x)))

)

PDDL – Domain File

•

Pickup Action:

(:action pickup

:

parameters (?x - box ?y - arm ?w - room)

precondition (and (free ?y) (robot-at ?w)

(

box-at ?x ?w))

effect (and (carry ?x ?y) (not (box-at ?x ?w))

not(free ?y)))

:

(

)

Putdown Action:

(:action putdown

:

parameters (?x - box ?y -arm ?w - room)

precondition (and (carry ?x ?y) (robot-at ?w))

effect (and (not(carry ?x ?y)) (box-at ?x ?w)

(free ?y))

)

PDDL – Domain File

define (domain robot)

(

:requirements :strips :equality :typing)

:types room box arm)

:constants left right - arm)

:predicates

(

robot-at ?x - room)

box-at ?x - box ?y - room)

free ?x - arm)

carry ?x - box ?y - arm)

)

(

:action move

:

parameters (?x ?y - room)

precondition (robot-at ?x)

effect (and (robot-at ?y) (not (robot-at ?x)))

)

(

:action pickup

:

parameters (?x - box ?y - arm ?w - room)

precondition (and (free ?y) (robot-at ?w) (box-at ?x ?w))

effect (and (carry ?x ?y) (not (box-at ?x ?w)) (not(free ?y)))

)

(

:action putdown

:

parameters (?x - box ?y -arm ?w - room)

precondition (and (carry ?x ?y) (robot-at ?w))

effect (and (not(carry ?x ?y)) (box-at ?x ?w) (free ?y))

)

PDDL – Problem File

•

Objects: rooms, boxes, and arms.

(:objects

room1 room2 - room

box1 box2 box3 box4 - box

left right - arm

)

Initial State: the robot and all boxes are at room 1.

(:init

(

robot-at room1)

box-at box1 room1)

box-at box2 room1)

box-at box3 room1)

box-at box4 room1)

free left)

free right)

)

PDDL – Problem File

•

Goal: all boxes must be at room 2.

(:goal

(

and (box-at box1 room2)

(box-at box2 room2)

(box-at box3 room2)

(box-at box4 room2)

)

PDDL – Problem File

(

define (problem robot1)

:domain robot)

(:objects

room1 room2 - room

box1 box2 box3 box4 - box

left right - arm

)

(

:init

(

robot-at room1)

box-at box1 room1)

box-at box2 room1)

box-at box3 room1)

box-at box4 room1)

free left)

free right)

)

(

:goal

(

and

(

box-at box1 room2)

box-at box2 room2)

box-at box3 room2)

box-at box4 room2)

)

PDDL – Planners

•

HSP Planner - https://github.com/bonetblai/hsp-planners

–

Heuristic Search Planner;

–

Compiled version for windows (cygwin):

http://edirlei.3dgb.com.br/aulas/ia_2013_1/HSP-Planner.zip

Online PDDL Planner:

–

Editor: http://editor.planning.domains/

–

Remote API: http://solver.planning.domains/

HSP Planner

•

Executing the planner:

–

hsp.exe robot-problem.pddl robot-domain.pddl

Extra parameters:

–

Search direction: -d backward ou forward

–

Search algorithm: -a bfs ou gbfs

HSP Planner

•

Forward search:

• Backward search:

(

PICKUP BOX1 LEFT ROOM1)

MOVE ROOM1 ROOM2)

PUTDOWN BOX1 LEFT ROOM2)

MOVE ROOM2 ROOM1)

PICKUP BOX2 LEFT ROOM1)

MOVE ROOM1 ROOM2)

PUTDOWN BOX2 LEFT ROOM2)

MOVE ROOM2 ROOM1)

PICKUP BOX3 LEFT ROOM1)

PICKUP BOX4 RIGHT ROOM1)

MOVE ROOM1 ROOM2)

(PICKUP BOX4 RIGHT ROOM1)

(PICKUP BOX3 LEFT ROOM1)

(MOVE ROOM1 ROOM2)

(PUTDOWN BOX4 RIGHT ROOM2)

(PUTDOWN BOX3 LEFT ROOM2)

(MOVE ROOM2 ROOM1)

(PICKUP BOX2 RIGHT ROOM1)

(PICKUP BOX1 LEFT ROOM1)

(MOVE ROOM1 ROOM2)

(PUTDOWN BOX2 RIGHT ROOM2)

(PUTDOWN BOX1 LEFT ROOM2)

PUTDOWN BOX3 LEFT ROOM2)

PUTDOWN BOX4 RIGHT ROOM2)

Online PDDL Planner

•

Resulting plan:

(

pickup box1 left room1)

move room1 room2)

putdown box1 left room2)

move room2 room1)

pickup box2 left room1)

move room1 room2)

putdown box2 left room2)

move room2 room1)

pickup box3 left room1)

move room1 room2)

putdown box3 left room2)

move room2 room1)

pickup box4 left room1)

move room1 room2)

putdown box4 left room2)

PDDL – Simple Game Situation

•

“The objective of the NPC is to kill the player, but he can't do

much without a weapon.”

–

The game world comprises three places: store, street and a house;

There is a gun at the store;

The NPC is at the street;

The player is at the house;

Gun

NPC

Player

House

Store

Street

PDDL – Simple Game Situation

(define (domain simplegame)

(:requirements :strips :equality :typing)

(:types location character enemy weapon)

(:predicates

(

at ?c ?l)

path ?l1 ?l2)

has ?c ?w)

dead ?c)

)

(:action go

:

parameters (?c - character ?l1 - location ?l2 - location)

precondition (and (at ?c ?l1) (path ?l1 ?l2))

effect (and (at ?c ?l2) (not (at ?c ?l1)))

)

(:action get

:

parameters (?c - character ?w - weapon ?l - location)

precondition (and (at ?c ?l) (at ?w ?l))

effect (and (has ?c ?w) (not (at ?w ?l)))

)

(:action kill

:

parameters (?c - character ?e - enemy ?w - weapon ?l - location)

precondition (and (at ?c ?l) (at ?e ?l) (has ?c ?w))

effect (and (dead ?e) (not(at ?e ?l)))

)

PDDL – Simple Game Situation

(

define (problem npc1)

:domain simplegame)

(

:objects

store street house - location

npc - character

player - enemy

gun - weapon

)

(

:init

(

at npc street)

at player house)

at gun store)

path store street)

path street store)

path street house)

path house street)

)

(

:goal

(

and

(

dead player)

)

Exercise 1

1

) Implement the PDDL domain and problem files to solve the following

problem: “A giant dragon is attacking the castle and John must find a

way to kill the dragon!”

strong weapon (magic bow)

weak weapon (sword)

inside

John

weak troll

Store

chest key

River

chest

strong dragon

Castle

Town

Forest

Cave

Additional information:

•

John can not leave a location if there is a alive enemy there;

The weak troll can be killed with the weak weapon (sword);

The chest is closed. It can be opened with the chest key;

There is a strong weapon inside of the chest (magic bow);

The dragon can only be killed with a strong weapon (the magic bow);

Automated Planning in Unity

•

The best way to add automated planning to a Unity project is

by implementing the planning algorithm directly in Unity.

–

Starting point: C# PDDL Parser - https://github.com/sunsided/pddl

Alternatively, we can use a modified version of the HSP

Planner (written in C) as a standard alone application that can

be executed by an Unity script to generate plans.

–

http://edirlei.3dgb.com.br/aulas/game-ai/HPS-Planner-Unity.zip

–

Not an efficient solution. Use it only for prototyping purposes.

•

Another option: use the online planning service API:

–

http://solver.planning.domains/

–

Limitations: internet connection, speed, server overload, ...

Automated Planning in Unity

•

Executing the HSP Planner in Unity:

using System.Diagnostics;

..

try{

Process plannerProcess = new Process();

Relative path of the HSP

exe in the project folder.

.

plannerProcess.StartInfo.FileName = "Planner/hsp2.exe";

plannerProcess.StartInfo.CreateNoWindow = true;

plannerProcess.StartInfo.Arguments = "Planner/game-problem.pddl

Planner/game-domain.pddl";

plannerProcess.StartInfo.UseShellExecute = false;

plannerProcess.StartInfo.RedirectStandardOutput = true;

plannerProcess.Start();

plannerProcess.WaitForExit();

while (!plannerProcess.StandardOutput.EndOfStream){

UnityEngine.Debug.Log(plannerProcess.StandardOutput.ReadLine());

}

catch (System.Exception e){

UnityEngine.Debug.Log(e);

Processes the plan actions

individually.

}

Automated Planning in Unity - Example

•

Simple Game Situation Example: “The objective of the NPC is

to kill the player, but he can't do much without a weapon.”

Player

House

Street

Gun

NPC

Store

public class PlanAction {

public string name;

public List<string> parameters;

public Status status;

Class to store and interpret planner

actions.

public PlanAction(string action){

string temp = "";

name = "";

parameters = new List<string>();

public enum Status { Ready,

Executing,

Completed

}

;

foreach (char c in action){

if (c == ' '){

if (name.Equals(""))

name = temp;

else

parameters.Add(temp);

temp = "";

}

else if (c == ')')

parameters.Add(temp);

else if (c != '(')

temp += c;

}

status = Status.Ready;

}

public class NPCPlanner : MonoBehaviour {

private List<PlanAction> plan;

private int currentAction;

private NavMeshAgent agent;

public WaypointInfo[] waypoints;

void Start(){

plan = new List<PlanAction>();

agent = GetComponent<NavMeshAgent>();

currentAction = 0;

[

public struct WaypointInfo

{

System.Serializable]

public string name;

public Transform waypoint;

}

try{

Process planner = new Process();

planner.StartInfo.FileName = "Planner/hsp2.exe";

planner.StartInfo.CreateNoWindow = true;

planner.StartInfo.Arguments = "Planner/game-problem.pddl

Planner/game-domain.pddl";

planner.StartInfo.UseShellExecute = false;

planner.StartInfo.RedirectStandardOutput = true;

planner.Start();

planner.WaitForExit();

while (!planner.StandardOutput.EndOfStream){

plan.Add(new PlanAction(planner.StandardOutput.ReadLine()));

}

}catch (System.Exception e){

UnityEngine.Debug.Log(e);

}

void Update(){

if (currentAction < plan.Count){

if (plan[currentAction].status == Status.Ready){

DoAction(plan[currentAction]);

}

if (plan[currentAction].status == Status.Executing){

CheckAction(plan[currentAction]);

}

if (plan[currentAction].status == Status.Completed){

currentAction++;

}

Just an example. Usually you

should play an animation.

}

void DoAction(PlanAction action){

if (action.name.Equals("GO")){

agent.destination = GetWaypoint(action.parameters[2]);

action.status = Status.Executing;

}

else if (action.name.Equals("GET")){

Destroy(GameObject.FindGameObjectWithTag(action.parameters[1]));

action.status = Status.Executing;

}

else if (action.name.Equals("KILL")){

Destroy(GameObject.FindGameObjectWithTag(action.parameters[1]));

action.status = Status.Executing;

}

void CheckAction(PlanAction action){

if (action.name.Equals("GO")){

if (IsAtDestionation())

action.status = Status.Completed;

}

else if (action.name.Equals("GET")){

action.status = Status.Completed;

Usually you need to wait

until the animation ends.

}

else if (action.name.Equals("KILL")){

action.status = Status.Completed;

}

Vector3 GetWaypoint(string name){

foreach (WaypointInfo wp in waypoints){

if (wp.name.Equals(name))

return wp.waypoint.position;

}

return Vector3.zero;

}

public bool IsAtDestionation(){

Same function implemented

in last lecture.

...

}

Exercise 2

2

) Create a scene to represent the world specified in Exercise 1. Then,

integrate the HSP Planner in the project and implement the actions

of the NPC John to execute the generated plan.

strong weapon (magic bow)

weak weapon (sword)

inside

John

weak troll

Store

chest key

River

chest

strong dragon

Castle

Town

Forest

Cave

Automated Planning in Games

Games that are know for using planning algorithms:

STRIPS-based action planning:

•

–

HTN-based action planning:

Automated Planning in Games

•

There are many possible applications for automated planning

in games:

–

Planning NPC actions;

Strategy planning;

Design, test, and evaluate puzzles;

Quest generation;

Interactive storytelling;

Hierarchical Generation of Dynamic

and Nondeterministic Quests

A combination of several story-related quests can be used to create

complex narratives. The structure of the game's narrative can be

represented as a hierarchy of quests.

•

–

Lima, E.S. Feijó, B., and Furtado, A.L. Hierarchical Generation of Dynamic and

Nondeterministic Quests in Games. International Conference on Advances in Computer

Entertainment Technology (ACE 2014).

Hierarchical Generation of Dynamic

and Nondeterministic Quests

Save family

Take home

Save wife

PPrrootteecctt hhoouussee

Escape

Go to hospital

Go to market

Get antidote

GGoo hhoommee

GGiivvee aannttiiddoottee ttoo wwiiffee

Kill wife

¬

has(player,antidote)

Ask old man for an antidote

Get antidote

Go home

¬has(player,antidote)

Hierarchical Generation of Dynamic

and Nondeterministic Quests

Publications:

•

Lima, E.S. Feijó, B., and Furtado, A.L. Hierarchical Generation of Dynamic and Nondeterministic Quests

in Games. International Conference on Advances in Computer Entertainment Technology, 2014.

Lima, E.S. Feijó, B., and Furtado, A.L. Player Behavior Modeling for Interactive Storytelling in Games. XV

Brazilian Symposium on Computer Games and Digital Entertainment, 2016 [Best Paper Award].

Lima, E.S. Feijó, B., and Furtado, A.L. Player Behavior and Personality Modeling for Interactive

Storytelling in Games. Entertainment Computing, Volume 28, December 2018, p. 32-48, 2018.