Software Architectures for Agents and Mobile Robots Hans-Dieter Burkhard Humboldt University Berlin...

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



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.



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



Example: Online Travel Agency Example: Online Travel Agency •



“Stimulus-Response”

Travel Agent: How does it work

Customer: Agent:

Specify wish(fill in form)

Prepare answer(select and present best matching offers)



“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)





CRN = CASE RETRIEVAL NETCRN = CASE RETRIEVAL NET



Advisory agent

Travel Agent: How could it work

Customer Agent

I would like to go for holidays.



Advisory agent


Customer Agent

I would like to go for holidays.

Fine.

Do you like swimming?



Advisory agent


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?



Advisory agent


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?



Advisory agent


Customer Agent

Good entertainment, exclusive bars, etc.

Wonderful.

And in the evening?



Advisory agent


Customer Agent


Sounds fantastic, is this like what you want?

(presents an offer)



Advisory agent


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)



Advisory agent


Customer Agent


So, let´s see. What´s aboutthat?

(presents another offer)



Advisory Agent needs:• Needs of “Stimulus Response” Agent (offers, capabilities, ...) as before


Dialog



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




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



Control of Autonomous Mobile RobotsProblem: Dynamically changing environments

“Autonomous agents in real environments”

Problems: Localization, Movements, Control



Classical distinction of agents (robots)• Reactive:

– Simple stimulus response behavior– No planning– No persistent states

• Deliberative– Complicated deliberation– Planning– Persistent states



Sense-Think-Act-Cycle, Persistency

Environment

senseexecute

thinkAgent

Persistentstates



Reactive Systems

• Obstacle avoidance by keeping distance

• Chess program ( ? - not “simple”)

select

senseexecute

thinkAgent

A: xxxB: yyyC: zzz

Sensor-Actor-Coupling



Deliberative SystemsWith 3 persistent states for worldmodel, goals, plans

updateexecute

selectAgent

worldmodel

goalmeans-ends

plan



Travel agent

Customer


input

Agent

Worldmodel:Discriminating customer

update

select Goal:Sell pricey

means-ends Plan:Show attractive offers etc.

executeoutput



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



Synchronization Problem

update

Simple Synchronization

selectmeans-ends

update

Problems for• dynamical environments• complex processes

select

means-ends Conflict



Question

ROBOT = AGENT INSIDE A BODY ?



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



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



Annual World Championships and Conferences

SimulationSimulation

RescueRescue

Sony leggedSony legged

Middle sizeMiddle size

Small sizeSmall size

HumanoidHumanoid

www.robocup.org



Simple Stimulus-Response BehaviorRun to the ball




LOOP worldmodel := perceive (input); commitment := deliberate (worldmodel); output := execute(commitment);

select

senseexecute

thinkAgent

A: xxxB: yyyC: zzz




Why are they acting: Triggering events

• Stimulus-Response– recent events in the environment

• Goal-directed– recent events in the environment – internal goals



Goal-directed BehaviorImprovement:

Anticipate future situations: Goal

x





x



Mental States• Concerning past:

Worldmodel

• Concerning future:

Commitment (goal, intention, plan, ...)

Mental states are persistent states:

Keep information for more than one cycle



Stimulus-Response with WorldmodelSimulate unobservable events: worldmodel




LOOP worldmodel_new := update (input, worldmodel_old); commitment := deliberate (worldmodel); output := execute(commitment);



Worldmodel• persistent state concerning the past:

Worldmodel (Belief)

worldmodel_new := update (input, worldmodel_old);

Preprocessing of input from sensory signals

+ =



Plan for CooperationCooperation using joint intention (double pass) Remark: Simulation of recent situation (world model )

needs knowledge about teammate´s intention



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

+ =



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



UtilityTime to reach the ball

(simulation of future)



Fastest player

to reach the ball

(simulation

of future)

Utility



UtilityAppropriate kick direction

(simulation of future)



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




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)



Option HierarchyPlaySoccer

Offensive Defensive . . .

Score OffsideTrapAttackChangeWings/1

DoublePass/2

DoublePass/1 ...

Dribble

Pass

Intercept

Run

... ...

... ...

... ...

... ...

. . .

...

. . .

...

. . .

...

Kick. . .

Reposition

... ...

... ...



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



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



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



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

?

?



Oscillation (Noisy Sensory Data)

+ =

?

?



Adaptation (Changing Plan)

+ =

?

?




+ =

?

?



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



Problems: Context ProblemPlaySoccer



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



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



Travel agent•

Customer Agent


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.



Problems: Least Commitment• Start: Partial Plan• Later: Exact Parameters ?

Needs consideration on all levels



Double Pass Architecture• Predefined Option Hierarchy

• Choosen Part of it: Intention subtree(choosen by Deliberator)

• Active Part of it: Activity path

(updated by Executor)





Score OffsideTrapAttackChangeWings/1DoublePass/2DoublePass/1 ...Dribble

Pass

Intercept

Run

... ...

... ...

... ...

... ...

. . .

...

. . .

...

. . .

...

Kick. . .

Reposition

... ...

... ...



Intention Subtree (chosen by Deliberator)

PlaySoccer



Pass

Intercept

Run

... ...

... ...

... ...

... ...

. . .

...

. . .

...

. . .

...

Kick . . .

Reposition

... ...

... ...



Activity Path: Active Options

PlaySoccer



Pass

Intercept

Run

... ...

... ...

... ...

... ...

. . .

...

. . .

...

. . .

...

Kick . . .

Reposition

... ...

... ...



Intention Subtree (chosen by Deliberator)

PlaySoccer



DoublePass/2

DoublePass/1

...Dribble

Pass

Intercept

Run

... ...

... ...

... ...

... ...

. . .

...

. . .

...

. . .

...

Kick . . .

Reposition

... ...

... ...



Activity Path: Active Options

PlaySoccer



DoublePass/2

DoublePass/1 ...

Dribble

Pass

Intercept

Run

... ...

... ...

... ...

... ...

. . .

...

. . .

...

. . .

...

Kick . . .

Reposition

... ...

... ...



“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)



Deliberator: Constructs Intention Subtree

PlaySoccer



DoublePass/2

DoublePass/1

...Dribble

Pass

Intercept

Run

... ...

... ...

... ...

... ...

. . .

...

. . .

...

. . .

...

Kick . . .

Reposition

... ...

... ...

Construction may need longer time



Executor-Pass through all levels

PlaySoccer



DoublePass/2

DoublePass/1 ...

Dribble

Pass

Intercept

Run

... ...

... ...

... ...

... ...

. . .

...

. . .

...

. . .

...

Kick . . .

Reposition

... ...

... ...

in each cycle through all levels




PlaySoccer



DoublePass/2

DoublePass/1 ...

Dribble

Pass

Intercept

Run

... ...

... ...

... ...

... ...

. . .

...

. . .

...

. . .

...

Kick . . .

Reposition

... ...

... ...




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






Pass

Intercept

Run

... ...

... ...

... ...

... ...

. . .

...

. . .

...

. . .

...

Kick. . .

Reposition

... ...

... ...



Synchronization (parallel work)

Sensors

Perception

Activity path

Actions

Sensors

Perception

Deliberation

Plan

Deliberator

Executor



Synchronization (sequential work)

Sensors

Perception

Deliberation

Plan

Deliberator

Sensors

Perception

Activity path

Actions

Executor



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)



In Progress

• Double Pass Architecture– Formal specification– Implementation

• Skills & Behaviors

THANK YOU ! THANK YOU !

Software Architectures for Agents and Mobile Robots Hans-Dieter Burkhard Humboldt University Berlin...

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