SYSC5103 Software Agents
RoboCup and BDI Architecture
Fall 2006
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•
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Yousif Al Ridhawi
Morvarid Sehatkar
Walter Kung
Gayathri Jayaraman
Outline
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•
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Multi-Agent System and BDI Architecture;
Jason and AgentSpeak(L);
RoboCup;
Design and Implementation of Jason
Environment;
• Design and Implementation of soccer player
behavior;
• Performance Results;
• Evaluate Jason as a BDI Programming system;
Multi-Agent System and BDI Architecture
• Multi-agent system is the sub field of artificial
intelligence, which studies systems involving multiple
agents and their coordination.
• When a group of agents in a multi-agent system share a
common long-term goal, they can be said to form a
team.
• The BDI architecture is being widely used in dynamic
and complex scenarios where agents may need to act
under incomplete and incorrect information about other
agents and the environment where they are situated.
• The BDI model has some philosophical basis in the
Belief-Desire-Intention theory of human practical
reasoning.
Jason and AgentSpeak(L)
• Jason is a Java-based platform for the development of multi-agent
systems
• Jason is one of the interpreter for an extended version of
AgentSpeak(L).
• AgentSpeak(L) is the abstract languages based on BDI architecture.
• AgentSpeak(L) maintains a set of beliefs and a set of plans.
• The set of beliefs represents the information an agent presently has
about the world
• A plan is a sequence of steps the agent needs to execute in order to
handle some perceived event.
• A AgentSpeak(L) plan has
–
–
–
–
–
A head: triggering event
A context: predicate for the plan to be considered applicable.
A body: a sequence of basic actions
head : context <- body
e.g. “+e : true <- !g”
RoboCup
• Soccer Server
– Provides a virtual field that
simulates all movements of a
ball and players.
– Send field information to
Clients via UDP/IP socket.
• Soccer Monitor
– Display graphical
representation of the game.
– Initial kick-off command.
• Clients
– client controls movements of
one player by sending
commands through UDP/IP
socket to the server.
Design and Implementation of Jason Environment
jason.environment.Environment
interface:
SendCommand
boolean executeAction(String ag, Term action)
move(double x, double y);
turn(double moment);
turn_neck(double moment);
dash(double power);
kick(double power, double direction);
say(String message);
void stop()
addPercept(Literal l)
void init(String a[])
void move(double x, double y)
void turn(double moment)
void turn_neck(double moment)void dash(double
power)
void kick(double power, double direction)
void say(String message)
void changeView(String angle, String quality)
void mainLoop(String[] a) throws IOException
void send(String message)
void see(VisualInfo info)
void hear(int time, String message)
void see(VisualInfo info);
void hear(int time, int direction, String message);
void hear(int time, String message);
NewBrain
SoccerPlayer
int isAction
int timedelay
NewBrain brain
SensorInput
1
n
VisualInfo
Memory
Vector m_objects
StringTokenizer m_tokenizer
String m_message
void store(VisualInfo info)
ObjectInfo getObject(String name)
void waitForNewInfo()
Vector getBallList()
Vector getPlayerList()
Vector getGoalList()
Vector getLineList()
Vector getFlagList()
void parse()
Communication between threads
Activity Chart
RoboCUP
Server
init()
Thread
NewBrain(n)
Thread
SoccerPlayer
Jason
Send "Init"
receive team side
Send player visual
info
Process actions
Add Perception to
Jason
Send actions
executeAction
handle the actions
Process
Perception.
Generate actions
Issues and Lessons Learned
• Issue: Perceptions were added before the previous action could be
completed.
Solution  Introduced a time delay between an action and the next
perception received.
• Open Issue: In multiplayer environment, multiple threads (one for
each player) access the same environment methods
resulting in socket overwrite.
Recommendation (from Jomi Hubner)  Use of Agent Architecture
class (for each agent) instead of using environment class (Work in
Progress)
• Open Issue: Lack of good explanation of the APIs and examples in
Jason documentation.
• Open Issue: Debugging the code using breakpoints.
State Diagram for Soccer Player Behavior
ball(null)
turn(20)
Checking(ball)
ba
ll(
D
is
ball(null)
to the goal
GDir - Direction
kick(GDir, 100)
ba
ll (
t,
D
ir,
nu
ll )
0,
0
)
Dist>1
Dash(25*Dist)
ta
nc
D ef
is ro
t> m
1 th
e
ba
ll.
Found(ball)
is
Checking(goal)
Dir <> 0
turn(Dir)
<=
ist
:d
is
t
kickoff
D
idle
1
D
Goal(null)
turn(20)
AgentSpeak(L) Code
//BELIEFS
checking(ball).
//PLANS
// If the ball is not visible. go to checking(ball) state
+ball(null) : true
<-ball(null);
-ball(Dist, Dir, DistChang, DirChng);
turn(40);
+checking(ball);
-checking(goal).
// If kickOff occurs.
+kickOff <?ball(Dist, Dir, DistChang, DirChng);
-ball(Dist, Dir, DistChang, DirChng).
// REACHBALL: The ball Dir is correct and dist is okay. Search for
the goal
+ball(Dist, Dir, DistChng, DirChng) : Dist <= 1
<+checking(goal);
!kick(goal);
-ball(null).
// Sub Goal 1: see goal, then kick ball
+!kick(goal): goal(GDist, GDir, GDistChng, GDirChng) &
checking(goal)
<kick(100, GDir);
-goal(GDist, GDir, GDistChng, GDirChng);
-checking(goal).
// Sub Goal 2: do not see goal, then search for goal
+!kick(goal): goal(null) & checking(goal)
<turn(40).
// The ball Dir is not correct, turn to match dir
+ball(Dist, Dir, DistChang, DirChng) : not (Dir = 0) & not
checking(goal)
<-ball(null);
turn(Dir).
// If ball is out of reach, run toward it.
+ball(Dist, Dir, DistChang, DirChng) : Dist > 1
<-ball(null);
-checking(goal);
-ball(Dist, Dir, DistChang, DirChng);
dash(Dist * 25);
.wait(kickOff).
// if I see the goal, that means is not goal(null)
+goal(GDist, GDir, GDistChng, GDirChng)
<-goal(null).
// vice versa
+goal(null)
<-goal(GDist, GDir, GDistChng, GDirChng).
Time Comparison
Jason Implementation vs Krislet
Time(ms) for first goal in each Trial Run
Jason Implementation vs Krislet
1200
SoccerPlayer
539
927
553
976
455
960
447
955
452
1019
465
967
1000
800
Time in ms
Krislet
600
400
200
522
915
486
1014
0
1
507
975
514
1047
2
3
4
5
Trial Run
SoccerPlayer
6
Krislet
7
8
9
10
BDI Programming System Requirements
• The programming language has to be
expressive
– Able to describe the agent behavior without regarding
how agent is implemented.
• Reasoning of system has to be flexible
– Able to decide, moment by moment, which action to
perform in the furtherance of its goals.
• Has to be responsive to the environment
– Interpreter must be fast.
– Must have correct response to all situations.
Expressive: AgentSpeak(L) describe agent using
abstract notions.
Checking(ball)
ba
ll(
D
is
t
nu
ll)
0,
0
)
Dist>1
Dash(25*Dist)
e
ba
ll.
Found(ball)
Di
st
// Turn if the agent do not see the goal.
+!kick(goal): goal(null) & checking(goal)
<- turn(40).
,D
ir,
ba
ll (
an
Di ce f
st ro
> m
1 th
kickoff
:d
idle
ball(null)
to the goal
GDir - Direction
kick(GDir, 100)
// Kick if the agent see the goal.
+!kick(goal):
goal(GDist, GDir, GDistChng, GDirChng) &
checking(goal)
<- kick(100, GDir);
-goal(GDist, GDir, GDistChng, GDirChng);
-checking(goal);
+checking(ball).
ball(null)
turn(20)
ist
// FOUND BALL: The ball Dir is correct and dist
is okay. Search for the goal
+ball(Dist, Dir, DistChng, DirChng) : Dist <= 1
<- +checking(goal);
!kick(goal);
-ball(null).
Checking(goal)
Dir <> 0
turn(Dir)
=
t<
is
D
1
Goal(null)
turn(20)
Flexibility : Deliberate to Select Achievable Option.
// Option 1: If the ball is in range, kick the ball
+ball(Dist, Dir, DistChng, DirChng) : Dist <= 1
<- +checking(goal);
!kick(goal);
-ball(null).
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The following perceptions are added
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–
•
There are two options that could be
triggered.
–
// Option 2: If the ball is in range, find goal
+ball(Dist, Dir, DistChng, DirChng) : Dist <= 1
<- +checking(goal);
!check(goal);
-ball(null).
// SubGoal 1: Kick ball to goal
+!kick(goal): goal(GDist, GDir, GDistChng,
GDirChng) & checking(goal)
<- kick(100, GDir);
-goal(GDist, GDir, GDistChng, GDirChng);
-checking(goal).
–
•
•
Option 1 has an unachievable sub goal
(!kick(goal)) which required the goal
location is known.
Option 2 has achievable sub-goal.
(!check(goal)).
The interpreter should pick option 2.
However, Jason pick option 1 and
generate the following error.
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–
–
–
// SubGoal 2: Check the goal location
+!check(goal): goal(null) & checking(goal)
<- turn(40).
+ball(0.5, 0, 0, 0)
+goal(null)
[test1]
Found a goal for which there is no
applicable plan:
+!kick(goal)
@l__4[source(self)]
+ball(Dist,Dir,DistChng,DirChng)[source(percept
)] : (Dist <= 1) <- !kick(goal); -ball(null). : {Dist=1,
Dir=0, DirChng=0, DistChng=0}
[test1] No fail event was generated for
+!kick(goal)
Flexibility (cont’)
•
// REACHBALL: The ball Dir is correct and dist is
okay. Search for the goal
+ball(Dist, Dir, DistChng, DirChng) : Dist <= 1
<- +checking(goal);
!kick(goal);
-ball(null).
// SubGoal 1: kick ball to goal
+!kick(goal): goal(GDist, GDir, GDistChng,
GDirChng) & checking(goal)
<- kick(100, GDir);
-goal(GDist, GDir, GDistChng,
GDirChng);
-checking(goal).
// SubGoal 2: check goal location
+!kick(goal): goal(null) & checking(goal)
<- turn(40).
Jason was successful in picking the
achievable plan if the condition is
specified in the context part of the
plan.
Responsive to the Environment
// If ball is out of reach, run toward it.
+ball(Dist, Dir, DistChang, DirChng) : Dist > 1
<- -ball(null);
-ball(Dist, Dir, DistChang, DirChng);
dash(Dist * 25).
RobuCup
Send Perception
Action
Processing
Jason
Send Command
Before Kick-Off
ball(20, 10, 0, 0)
dash(500)
Dash(500) and
failed
ball(20, 10, 0, 0)
No response to the
old perception
• Jason handled goal as event.
• When event occurs, it triggers
plans.
• Advantage: responds quickly
to the environment. It behaves
like reflex action.
• Disadvantage: there is no
check whether the goal is
accomplished or not.
• Jason does not respond to
incomplete goal.
Conclusions
• Successfully implement soccer player in
RoboCUP using BDI development framework.
• Noticeable performance difference in terms of
speed between the Java code implementation of
Krislet and the Jason version;
• Agent Speak(L) can express agent behavior
using abstract notions;
• Jason BDI reasoning engine is not flexible with
the option deliberation;
• Jason has no verification whether the goal has
been accomplished;
Backup slide: Jason Environment
Player Actions
Soccer Field
Visual Info
RoboCup Server
Jason Environoment
Intension
Perceptions
Intention
Selection
BDI Engine
Belief
Base
Plan
Library
Actions
Backup slide: An interpretation cycle of an AgentSpeak(L) program