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Software Architectures for Agents and Mobile Robots
Hans-Dieter BurkhardHumboldt University BerlinInstitute of Informatics
www.ki.informatik.hu-berlin.de
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 2
Topics of the talkSoftware Architectures
for Agents and Mobile Robots
• AI at Humboldt University
• Agents & Robots
• Architectures
• Mental states
• Control, Planning
• Double Pass Architecture
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 3
Artificial Intelligence at Humboldt University
Understanding emerges by doing.
Applied to the study of mental processes, this means modeling of intelligent behavior by machines.
Artificial Intelligence has two aspects: First modeling with the goal of better understanding, and second engineering of useful machines.
Understanding emerges by doing.
Applied to the study of mental processes, this means modeling of intelligent behavior by machines.
Artificial Intelligence has two aspects: First modeling with the goal of better understanding, and second engineering of useful machines.
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 4
Artificial Intelligence at Humboldt University
Case Based Reasoning
Knowledge Management
Agent Oriented Techniques
Distributed AI
Socionics
Applications in Medicine
Intelligent Robotics
www.ki.informatik.hu-berlin.deEnglish version
English version
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 5
Example: Online Travel Agency Example: Online Travel Agency •
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 6
“Stimulus-Response”
Travel Agent: How does it work
Customer: Agent:
Specify wish(fill in form)
Prepare answer(select and present best matching offers)
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 7
“Stimulus-Response” Agent needs:• Knowledge about
– offers (data base)– similarity (acceptable alternative offers)
• Capabilities to– Update offers– Interaction with customer– Search of best matching offers
( Case Retrieval Nets)
Travel Agent: How does it work
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 8
Travel Agent: How does it work
CRN = CASE RETRIEVAL NETCRN = CASE RETRIEVAL NET
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 9
Advisory agent
Travel Agent: How could it work
Customer Agent
I would like to go for holidays.
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 10
Advisory agent
Travel Agent: How could it work
Customer Agent
I would like to go for holidays.
Fine.
Do you like swimming?
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 11
Advisory agent
Travel Agent: How could it work
Customer Agent
Yes, I like to be with my friend on a white strand, no other tourists.And I enjoy sports.
Fine.
Do you like swimming?
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 12
Advisory agent
Travel Agent: How could it work
Customer Agent
Yes, I like to be with my friend on a white strand, no other tourists.And I enjoy sports.
Wonderful.
And in the evening?
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 13
Advisory agent
Travel Agent: How could it work
Customer Agent
Good entertainment, exclusive bars, etc.
Wonderful.
And in the evening?
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 14
Advisory agent
Travel Agent: How could it work
Customer Agent
Good entertainment, exclusive bars, etc.
Sounds fantastic, is this like what you want?
(presents an offer)
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 15
Advisory agent
Travel Agent: How could it work
Customer Agent
Looks fantastic.But it is far behind of my financial limits, may be less exclusive.
Sounds fantastic, is this like what you want?
(presents an offer)
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 16
Advisory agent
Travel Agent: How could it work
Customer Agent
Looks fantastic.But it is far behind of my financial limits, may be less exclusive.
So, let´s see. What´s aboutthat?
(presents another offer)
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 17
Advisory Agent needs:• Needs of “Stimulus Response” Agent (offers, capabilities, ...) as before
Travel Agent: How could it work
Dialog
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 18
Advisory Agent needs:
• “Dynamic” knowledge about dialog with customer
– History of dialog
– (Hypothetical) Model of current customer • Wishes, intentions• Capabilities• Beliefs
– (Flexible) Plan for • Discovering customer´s wishes, intentions, ...• Selling most valuable products
Travel Agent: How could it work
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 19
Agent Oriented Techniques
• Information agents • Autonomous systems• Cooperative systems
• Socionics: humans + autonomous machines– Cooperation – Sociological requirements– Organizational aspects
“Agents work autonomously on behalf of their users.”
Autonomy: Following „own“ rules (example: chess program)
• Autonomy w.r.t. somebody• Complexity of decisions
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 20
Control of Autonomous Mobile RobotsProblem: Dynamically changing environments
“Autonomous agents in real environments”
Problems: Localization, Movements, Control
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 21
Classical distinction of agents (robots)• Reactive:
– Simple stimulus response behavior– No planning– No persistent states
• Deliberative– Complicated deliberation– Planning– Persistent states
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 22
Sense-Think-Act-Cycle, Persistency
Environment
senseexecute
thinkAgent
Persistentstates
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 23
Reactive Systems
• Obstacle avoidance by keeping distance
• Chess program ( ? - not “simple”)
select
senseexecute
thinkAgent
A: xxxB: yyyC: zzz
Sensor-Actor-Coupling
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 24
Deliberative SystemsWith 3 persistent states for worldmodel, goals, plans
updateexecute
selectAgent
worldmodel
goalmeans-ends
plan
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 25
Travel agent
Customer
Good entertainment, exclusive bars, etc.
input
Agent
Worldmodel:Discriminating customer
update
select Goal:Sell pricey
means-ends Plan:Show attractive offers etc.
executeoutput
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 26
Unfolding the cycle
Worldmodel Worldmodel
Goal
Plan
update
execute
means-ends
select
Goal
Plan
update
execute
means-ends
select
Worldmodel
Goal
Plan
update
execute
means-ends
select
updateexecute
selectAgent
worldmodel
goalmeans-ends
plan
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 27
Synchronization Problem
update
Simple Synchronization
selectmeans-ends
update
Problems for• dynamical environments• complex processes
select
means-ends Conflict
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 28
Question
ROBOT = AGENT INSIDE A BODY ?
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 29
Simple architectures for physical agents
Stimulus-Response– Immediate reactions to inputs from the real world.– „The best model of the world is the world itself.“
Braitenberg
Vehicle
No need for acomplex agent inside the robot
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 30
Soccer Playing Robots By the year 2050,
develop a team of fully autonomous humanoid robots that can win against the human world soccer champion team.
ENIAC1946
Deep Blue1997
Test field for Goal driven research
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 31
Annual World Championships and Conferences
SimulationSimulation
RescueRescue
Sony leggedSony legged
Middle sizeMiddle size
Small sizeSmall size
HumanoidHumanoid
www.robocup.org
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 32
Simple Stimulus-Response BehaviorRun to the ball
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Software Architectures for Agents and Mobile Robots 33
Simple Stimulus-Response BehaviorRun to the ball
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 34
Simple Stimulus-Response BehaviorRun to the ball
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 35
Simple Stimulus-Response BehaviorRun to the ball
LOOP worldmodel := perceive (input); commitment := deliberate (worldmodel); output := execute(commitment);
select
senseexecute
thinkAgent
A: xxxB: yyyC: zzz
Sensor-Actor-Coupling
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 36
Why are they acting: Triggering events
• Stimulus-Response– recent events in the environment
• Goal-directed– recent events in the environment – internal goals
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 37
Goal-directed BehaviorImprovement:
Anticipate future situations: Goal
x
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Software Architectures for Agents and Mobile Robots 38
Goal-directed BehaviorImprovement:
Anticipate future situations: Goal
x
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 39
Goal-directed BehaviorImprovement:
Anticipate future situations: Goal
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 40
Mental States• Concerning past:
Worldmodel
• Concerning future:
Commitment (goal, intention, plan, ...)
Mental states are persistent states:
Keep information for more than one cycle
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 41
Stimulus-Response with WorldmodelSimulate unobservable events: worldmodel
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Software Architectures for Agents and Mobile Robots 42
Stimulus-Response with WorldmodelSimulate unobservable events: worldmodel
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 43
Stimulus-Response with WorldmodelSimulate unobservable events: worldmodel
LOOP worldmodel_new := update (input, worldmodel_old); commitment := deliberate (worldmodel); output := execute(commitment);
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 44
Worldmodel• persistent state concerning the past:
Worldmodel (Belief)
worldmodel_new := update (input, worldmodel_old);
Preprocessing of input from sensory signals
+ =
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 45
Plan for CooperationCooperation using joint intention (double pass) Remark: Simulation of recent situation (world model )
needs knowledge about teammate´s intention
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 46
Plan for CooperationCooperation using joint intention (double pass) Remark: Simulation of recent situation (world model )
needs knowledge about teammate´s intention
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 47
Plan for CooperationCooperation using joint intention (double pass) Remark: Simulation of recent situation (world model )
needs knowledge about teammate´s intention
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 48
Plan for CooperationCooperation using joint intention (double pass) Remark: Simulation of recent situation (world model )
needs knowledge about teammate´s intention
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 49
Plan for CooperationCooperation using joint intention (double pass) Remark: Simulation of recent situation (world model )
needs knowledge about teammate´s intention
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 50
Plan for CooperationCooperation using joint intention (double pass) Remark: Simulation of recent situation (world model )
needs knowledge about teammate´s intention
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 51
Commitments: Goal-Directed Architecture
Difference to Stimulus Response: • Persistent state concerning the future (commitment: goal, plan ...)
LOOP worldmodel_new := update (input, worldmodel_old); commitment_new := deliberate (worldmodel_new,commitment_old); output := execute (commitment_new);
Commitment_old new alternatives Commitment_new
+ =
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 52
AT Humboldt 98 (Simulation league)
• worldmodel• intentions• plans
utilities
Player
worldmodel
deliberation
skills
options
kick
intercept dribblepass
Pass to teammateKick to goal
Dribble Go to position
Intercept. . .
kick
interceptdribblepass
options
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 53
UtilityTime to reach the ball
(simulation of future)
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Software Architectures for Agents and Mobile Robots 54
Fastest player
to reach the ball
(simulation
of future)
Utility
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Software Architectures for Agents and Mobile Robots 55
UtilityAppropriate kick direction
(simulation of future)
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 56
Problems: Time Trade-Off• Fast decision
– newest data– rough criteria
• Complex deliberation– detailed analysis, long term plans– synchronization problem
updateexecute
selectAgent
worldmodel
goalmeans-ends
plan
update
select
means-ends conflict
think
select
senseexecute
thinkAgent
A: xxxB: yyyC: zzz
Sensor-Actor-Coupling
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 57
Problems: Time Trade-Off– Fast decision
vs.– Complex deliberation
Architectures with different levels (layers)
Need for balance between –low level (Stimulus-Response)–high level (Goal-directed)
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 58
Option HierarchyPlaySoccer
Offensive Defensive . . .
Score OffsideTrapAttackChangeWings/1
DoublePass/2
DoublePass/1 ...
Dribble
Pass
Intercept
Run
... ...
... ...
... ...
... ...
. . .
...
. . .
...
. . .
...
Kick. . .
Reposition
... ...
... ...
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 59
Choice-Option (“OR-Branching”)
State (Place)
Current State (marked Place)
conditionTransition with condition
finished orcanceled
finished orcanceled
Offensive
Score DoublePass/2DoublePass/1
...
MaxUtility MaxUtilityMaxUtility...
ball out ofkickrange
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 60
Sequence-Option (“AND-Branching”)
Pass finished
Teammate free
Dribble Pass InterceptRun
Teammate finished Pass
Reposition
Teammate passes
State (Place)
Current State (marked Place)
conditionTransition with condition
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 61
Extension for “unexpected” situation
finished orcanceled
finished orcanceled
Offensive
Score DoublePass/2DoublePass/1
...
MaxUtility MaxUtilityMaxUtility...
ball out ofkickrange
ball control& goal free
Additional transitions (with simple conditions)
problem withteam mate
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 62
Problems: Stability Trade-Off
• Stabile behavior+ achieve goals + reliability in cooperation fanatism
• Adaptation to new situation+ flexibility oscillation re-planning
+ =commitment_old new alternatives commitment_new
?
?
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 63
Oscillation (Noisy Sensory Data)
+ =
?
?
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Software Architectures for Agents and Mobile Robots 64
Adaptation (Changing Plan)
+ =
?
?
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Software Architectures for Agents and Mobile Robots 65
Adaptation (Changing Plan)
+ =
?
?
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 66
Adaptation (Changing Plan)
+ =
?
?
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 67
Adaptation (Changing Plan)
+ =
?
?
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 68
Adaptation (Changing Plan)
+ =
?
?
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 69
Problems: Stability Trade-Off• Stabile behavior
vs.• Adaptation to new situation
– persistent state concerning future– bias for old behavior (preventing from oscillation)
Need for balanced re-deliberation
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 70
Problems: Context ProblemPlaySoccer
Offensive Defensive . . .
Score OffsideTrapAttackChangeWings/1
DoublePass/2
DoublePass/1 ...
Dribble
Pass
Intercept
Run
... ...
... ...
... ...
... ...
. . .
...
. . .
...
. . .
...
Kick. . .
Reposition
... ...
... ...
Example:
(Opponent behaves in unexpected way)• Active Behavior: inside Dribbling• Invalid Condition for: Double Pass
Need for re-consideration on all levels Problem for stack oriented runtime systems
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 71
Stack oriented architectures• Classical architectures are stack oriented
– Only the procedure on top of stack is active
i.e., only low level behavior
– Higher level behavior can become active only when
lower levels are finished/interrupted
Intentions may change on any level- caused by external events
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 72
Travel agent•
Customer Agent
Looks fantastic.But it is far behind of my financial limits, may be less exclusive.
Intentions may change on any level- caused by external events
Ooops – no chance to sell pricey ...
Worldmodel:No Discriminating customer
Goal:Sell pricey
Plan:Show attractive offers etc.
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 73
Problems: Least Commitment• Start: Partial Plan• Later: Exact Parameters ?
Needs consideration on all levels
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 74
Double Pass Architecture• Predefined Option Hierarchy
• Choosen Part of it: Intention subtree(choosen by Deliberator)
• Active Part of it: Activity path
(updated by Executor)
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 58
Option HierarchyPlaySoccer
Offensive Defensive . . .
Score OffsideTrapAttackChangeWings/1DoublePass/2DoublePass/1 ...Dribble
Pass
Intercept
Run
... ...
... ...
... ...
... ...
. . .
...
. . .
...
. . .
...
Kick. . .
Reposition
... ...
... ...
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 75
Intention Subtree (chosen by Deliberator)
PlaySoccer
Offensive Defensive . . .
Score OffsideTrapAttackChangeWings/1DoublePass/2DoublePass/1 ...Dribble
Pass
Intercept
Run
... ...
... ...
... ...
... ...
. . .
...
. . .
...
. . .
...
Kick . . .
Reposition
... ...
... ...
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 76
Activity Path: Active Options
PlaySoccer
Offensive Defensive . . .
Score OffsideTrapAttackChangeWings/1DoublePass/2DoublePass/1 ...Dribble
Pass
Intercept
Run
... ...
... ...
... ...
... ...
. . .
...
. . .
...
. . .
...
Kick . . .
Reposition
... ...
... ...
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 75
Intention Subtree (chosen by Deliberator)
PlaySoccer
Offensive Defensive . . .
Score OffsideTrapAttackChangeWings/1
DoublePass/2
DoublePass/1
...Dribble
Pass
Intercept
Run
... ...
... ...
... ...
... ...
. . .
...
. . .
...
. . .
...
Kick . . .
Reposition
... ...
... ...
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 76
Activity Path: Active Options
PlaySoccer
Offensive Defensive . . .
Score OffsideTrapAttackChangeWings/1
DoublePass/2
DoublePass/1 ...
Dribble
Pass
Intercept
Run
... ...
... ...
... ...
... ...
. . .
...
. . .
...
. . .
...
Kick . . .
Reposition
... ...
... ...
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 77
“Doubled One-Pass-Architecture”– Deliberator-Pass (“goal-oriented”)
builds Intention Subtree
one deliberator pass may work over several cycles
– Executor-Pass (“stimulus-response”)
traverses and adjusts Activity Path
limited search space by Intention subtree
one executor pass per cycle
• Differences to “classical” programming– Control flow by deliberation (“agent oriented”)– Double Pass Runtime Organization (not by stacks)
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 78
Deliberator: Constructs Intention Subtree
PlaySoccer
Offensive Defensive . . .
Score OffsideTrapAttackChangeWings/1
DoublePass/2
DoublePass/1
...Dribble
Pass
Intercept
Run
... ...
... ...
... ...
... ...
. . .
...
. . .
...
. . .
...
Kick . . .
Reposition
... ...
... ...
Construction may need longer time
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 79
Executor-Pass through all levels
PlaySoccer
Offensive Defensive . . .
Score OffsideTrapAttackChangeWings/1
DoublePass/2
DoublePass/1 ...
Dribble
Pass
Intercept
Run
... ...
... ...
... ...
... ...
. . .
...
. . .
...
. . .
...
Kick . . .
Reposition
... ...
... ...
in each cycle through all levels
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 80
Executor-Pass through all levels
PlaySoccer
Offensive Defensive . . .
Score OffsideTrapAttackChangeWings/1
DoublePass/2
DoublePass/1 ...
Dribble
Pass
Intercept
Run
... ...
... ...
... ...
... ...
. . .
...
. . .
...
. . .
...
Kick . . .
Reposition
... ...
... ...
in each cycle through all levels
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 81
Executor-Pass through all levels
PlaySoccer
Offensive Defensive . . .
Score OffsideTrapAttackChangeWings/1
DoublePass/2
DoublePass/1 ...
Dribble
Pass
Intercept
Run
... ...
... ...
... ...
... ...
. . .
...
. . .
...
. . .
...
Kick . . .
Reposition
... ...
... ...
in each cycle through all levels
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 82
Executor-Pass through all levels
PlaySoccer
Offensive Defensive . . .
Score OffsideTrapAttackChangeWings/1
DoublePass/2
DoublePass/1 ...
Dribble
Pass
Intercept
Run
... ...
... ...
... ...
... ...
. . .
...
. . .
...
. . .
...
Kick . . .
Reposition
... ...
... ...
in each cycle through all levels
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 83
Executor-Pass through all levels
PlaySoccer
Offensive Defensive . . .
Score OffsideTrapAttackChangeWings/1
DoublePass/2
DoublePass/1 ...
Dribble
Pass
Intercept
Run
... ...
... ...
... ...
... ...
. . .
...
. . .
...
. . .
...
Kick . . .
Reposition
... ...
... ...
in each cycle through all levels
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 84
Double Pass Architecture• Predefined Option Hierarchy• Deliberator
– long term deliberation (not time critical)– commitment for intentions: intention subtree
• Executor – short term reconsideration (time critical)– performs intentions on the activity path
Both working top-down from root to leaves
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 58
Option HierarchyPlaySoccer
Offensive Defensive . . .
Score OffsideTrapAttackChangeWings/1DoublePass/2DoublePass/1 ...Dribble
Pass
Intercept
Run
... ...
... ...
... ...
... ...
. . .
...
. . .
...
. . .
...
Kick. . .
Reposition
... ...
... ...
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 85
Synchronization (parallel work)
Sensors
Perception
Activity path
Actions
Sensors
Perception
Deliberation
Plan
Deliberator
Executor
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 86
Synchronization (sequential work)
Sensors
Perception
Deliberation
Plan
Deliberator
Sensors
Perception
Activity path
Actions
Executor
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 87
Double Pass Architecture: Objectives
• Balance between low level/high level
- Time Trade-off• Balanced Re-deliberation
- Stability Trade-Off• Re-consideration on all levels
- Context Problem
- Least Commitment Problem
Long Term Research Goal:Learning of complex behavior (Case Based Reasoning)
H.D.Burkhard, HU Berlin MOCA 2002
Software Architectures for Agents and Mobile Robots 88
In Progress
• Double Pass Architecture– Formal specification– Implementation
• Skills & Behaviors
THANK YOU ! THANK YOU !