© 2004 ipd, prof. lockemann universität karlsruhe (th) sofsem04 flexibility through multiagent...

© 2004 IPD, Prof. Lockemann

UniversitätKarlsruhe (TH)

SOFSEM04

Flexibility ThroughMultiagent Systems:Solution or Illusion?

Peter C. Lockemann


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SOFSEM04

OEM

Dispoweb

KRASH-MAS

IntaPS-MAS

FABMAS-MAS

dispowebdispowebATT/SCC

intraplant interplant external

Mon

itorin

gExe

cutio

nPlann

ing

Customer

Global planning and negotiations

Production planning for electronics

Assembly planning

Production planning for mechanics

Tracking

Scenario: Production and supply chain


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SOFSEM04

KRASH-MAS

ATT-MAS

DISPOWEB-MAS

Tracing

Planing

IntaPS-MAS

ATT-MAS

ATT-MAS

DISPOWEB-MAS

FABMAS

DISPOWEB-MAS

Negotiation of the global production schedule

Tracing of processes and propagation of delays

SCC-MAS

PlaningExe-cutionPlaning

Tracing

Exe-cution

Tracing

Exe-cution

KRASH-MAS

ATT-MAS

DISPOWEB-MAS

Tracing

Planing

IntaPS-MAS

ATT-MAS

ATT-MAS

DISPOWEB-MAS

FABMAS

DISPOWEB-MAS

Negotiation of the global production schedule

Tracing of processes and propagation of delays

SCC-MAS

PlaningExe-cutionPlaning

Tracing

Exe-cution

Tracing

Exe-cution



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SOFSEM04


Nr. Activity (Actor)

Negotiate initial plan of production between supply chain partners (DISPOWEB).

Operational assembly planning (KRASH). Production planning for e.g. mechanical parts (IntaPS). Production planning for e.g. electronic parts (FABMAS).

Monitoring of orders and related suborders (ATT). Trigger internal planning systems in case of minor critical events (ATT). Next Trigger re-planning by DISPOWEB agents in case of a severe critical event (ATT). Next Controlling information is forwarded to trusted third party SCC-system (ATT/SCC).

Internal rescheduling in reaction to a critical event (KRASH, IntaPS, FABMAS). Next

Renegotiate plan of production between supply chain partners due to severe critical event (DISPOWEB). Next


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SOFSEM04

(Referring) Physicians

TU Ilmenau/Physicians Thuringia, OnkoNet

Ambulance

TU Ilmenau/Ambulances Thuringia

Reception Ward

Uni Hohenheim/Hosp. Sindelfingen

Kliniken & Stationen

Gastro-Enterology

Uni Würzburg/Univ.-Hosp. Würzburg

Cancerology

TU Ilmenau/Univ.-Hosp. KIM II Jena

Internal Medicine

Uni Hohenheim/Hosp. Sindelfinden

Surgery Ward

Uni Mannheim/Hosp. Pegnitz, Hosp. Kulmbach

N.N.

(…/…)

Intensive Care Unit

TU Berlin/Hosp. Charité Berlin

Cardiological Laboratory

Uni Mannheim/Hosp. Pegnitz & Hosp. Kulmbach

Transport Serv.

Uni Freiburg/Univ.-Hosp. Freiburg

Operating Theatre

Surgery Block

Univ Trier/Hosp.Barmh. Brüder Trier

Op.-Planning

TU Berlin/Charité Berlin

Dept. of Surgery

Uni Mannheim/Hosp. Pegnitz & Hosp. Kulmbach

Emergency Dept.

TU Berlin/Hosp. Charité Berlin

Radiology & Radiotherapy

Conv., CT, MRT

Uni Mannheim/Hosp. Pegnitz, Hosp. Kulmbach

Radiodiagnosis

Uni Freiburg/Univ.-Hosp. Freiburg

Radiotherapy

Uni Würzburg/Univ.-Hosp. Würzburg

N.N.

(…/…)

N.N.

(…/…)

Scenario: Health care


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Scenario: Digital libraries

The UniCats environment

providercustomer wrapper

HTTP

trader

provider registration

user agent

HTTP

query registration and result presentation

queries to traders and trader recommendations

HTTP

HTTP

queries to wrappers and query results

order and delivery


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SOFSEM04

Scenario: Traffic prognosis


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SOFSEM04

Scenario: Traffic prognosis

ADAC

POLIZEI

M essdaten Em pfehlungen

Reiseplan

Benutzer-Profile

Logistik-Daten G roßveransta ltung

Services

Real traffic

Model world


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SOFSEM04

The optimist

Social organizations are resilient and responsive to ever changing situations because they include enough intelligent decision makers sufficient slack to take decisions.

Then why not mirror these properties to build software systems that are flexible (resilient and responsive)?

Agents are a most natural metaphor to model complex social organizations that must be highly adaptive and rely on flexible and adaptive members. Such systems are known from AI and referred to as

multiagent systems (MAS). Hypothesis: If well done, MAS offer the adaptivity and

flexibility needed for the modeled world.


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SOFSEM04

The pessimist

Multiagent systems are distributed

include asynchronous communication

have components with indeterministic behavior.

Therefore, they are inherently difficult to engineer: It is hard to give a sufficiently precise system specification

and even if there is one, what are the chances to verify or validate that the system implementation satisfies the specification?


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SOFSEM04

Where the two may meet

Are there situations with compelling reasons for a multiagent approach?

Can one yet impose constraints to be able to engineer multiagent systems?


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SOFSEM04

Agenda

Are there situations with compelling reasons for a multiagent approach? What is an agent after all? ... and a multiagent system? Useful multiagent software systems: A hypothesis Testing the hypothesis Mixed results and a refined hypothesis

Can one yet impose constraints to engineer MAS? A database person’s view At least it should be reliable! Transactional agents Reliable agent cooperation

Conclusions and challenges


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SOFSEM04

Flexibility

What is an agent?

Wooldrige and Jennings on agents:

“An agent is a computer system that is situated in some environment, and that is capable of autonomous action in its environment in order to meet its design objectives.“

Wooldrige on intelligent agents:

“An intelligent agent is a reactive, proactive, interacting agent.“


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SOFSEM04

An open system: must have an observable

effect

Effective indeterminism in time

and kind

What’s behind the definition

“An agent is a computer system that is situated in some environment, and that is capable of autonomous action in its environment in order to meet its design objectives.“

“An intelligent agent is a reactive, proactive, interacting agent.“

Agents can be many things to many people

A piece of code: software agent

Stimulators and responders


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Multiagent systems

autonomoussituatedreactive

proactive

interacting interacting

interacting


proactive


proactive

Individual objectives

Common

objective


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SOFSEM04

Multiagent systems


proactive


interacting


proactive


proactive

Individual objectives

Common

objective

Can the systems be engineered?

How much flexibility can we manage?

Specialize!

Specialize!

Specialize!

Specialize!


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SOFSEM04

the programmer’s view

the software architect’s

view

the database person’s view

Who needs all that flexibility?

Hypothesis:

Multiagent systems are useful for an environment where

the range of situations (the problem space) is too large for enumeration and instead needs a qualitative description,

the problem space can be divided into sets of simpler tasks, each requiring specialized competence,

the simpler tasks can be dealt with autonomously by individual agents,

solving the overall situation requires cooperation among the agents.

the AI person’s view


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SOFSEM04

Flexibility through multiagent systems


proactive


interacting


proactive


proactiveDescrip

tive

competence

Descriptiv

e

competence

Descriptiv

e

competence

Assignment of individual

responsibilities &

cooperative problem solving


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SOFSEM04

Testing the hypothesis: Scenario

MoldMaintenance

Product Assembly1

Product Assembly2

Product Assembly3

SupplierBuffer1

Buffer

MoldBuffer

A1

A2

A3

A4

A5

B1

B2

B3

B4

B5

C1

C2

C3

C4

C5

Shipping

Molding AreaProduct Assembly Area Buffer

UL01

UL02

ImmidiateBuffer2

UG01Immidiate

Buffer

UG02

UH01SupplierBuffer2 UC02

UC01

UB02

UB01UA01

UA02

UF02

UF01

Unit Assembly Area

Option


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SOFSEM04

Benchmark

Mo

ldin

g a

rea

Bu

fferProduct assembly

A

B

C

Unit assembly

Ma

nu

ala

sse

mb

ly

External supplier

SupplyOrder

Kanban

Flexibility via Kanban production organization: Production control: buffer minimization via pull principle

High variability within homogeneous product spectrum


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SOFSEM04

Benchmark

Mo

ldin

g a

rea

Bu

fferProduct assembly

A

B

C

Unit assembly

Ma

nu

ala

sse

mb

ly

External supplier

SupplyOrder

Kanban

Analytical and simulation-based layout planning for a given production program

Externally determined production schedule


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SOFSEM04

Test and Benchmark

Centrally planned Kanban is not flexible enough to deal with machine failures

short-term deviations from the delivery schedule

Solution options Passive: Simulate disturbances and adjust layout planning

Reactive: Rescheduling algorithms (known to be suboptimal)

Reactive: Agents

Comparison by simulation


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SOFSEM04

The best performing agent model

Mixed-model Assembly Line Balancing Problem (MALBP):

Reactive MAS Approach

Order Data

Order Agent Machine Agent

4: Task Announcement Bidding

6: Bid Processing

3: Task Announcement

2: Request

5: Bid

1: New Order

7: Reporting Results


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SOFSEM04

Experiments by simulation

Parameter variations: Master data:

Bill of materials Operation list fore each product

Transport lot size Affects the number of suborders for a single order

Production order generated by production planning and control

Disturbance profile per machine (statistical) Interval between disruptions Disruption duration

Output: Throughput:

Average Standard deviation


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SOFSEM04

Experiments by simulation

Throughput and Processing TimeCorresponding Standard Deviations

Parameterisation


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SOFSEM04

Experiments: Sample Analysis

Throughput Time

Standard Deviation

Suitability of MAS Compared to Centralized OR Approaches Depending on Decision Variables

Reliability and Predictability of the Results

5 min 30 min 60 min1

3

5

Disruption Duration

Lot Size

Throughput Time

1-1,5

0,5-1

0-0,5

5 min 30 min 60 min1

3

5

Disruption Duration

Lot Size

Standard Deviation

1-1,5

0,5-1

0-0,5

-1-0

Improvement by MAS


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SOFSEM04

Empirical results

Multiagent systems need slack: If assembly lines run close to capacity, MAS are even

inferior. They performed better if slack was provided by additional

machines or by delaying assembly orders.

Multiagent systems need inhomogeneity: More assembly lines by themselves have no effect unless the

machines follow very different disturbance profiles. Different disturbance profiles have even a positive effect if no

machines are added.

Multiagent systems operate best in a dynamic, non-deterministic environment: MAS are superior when it comes to evening out fluctuations

due to disruptions.

This cannot solely be explained by the original hypothesis


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SOFSEM04

Revisiting the hypothesis

Hypothesis:

Multiagent systems are useful for an environment where the range of situations (the problem space) is too large for

enumeration and instead needs a qualitative description, the range of decisions (the solution space) is

commensurate in size with the problem space, the problem space can be divided into sets of simpler

tasks, each requiring specialized competence, the simpler tasks can be dealt with autonomously by

individual agents, solving the overall situation requires cooperation among

the agents.


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SOFSEM04

Practical implementation

DB

eMPlant Simulation Model

Communication Platform using Event Mechanisms

FIPA-OS Implementation


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SOFSEM04

A database persons’ view

Hypothesis:

Multiagent systems are useful for an environment where the range of situations (the problem space) is too large for

enumeration and instead needs a qualitative description, the range of decisions (the solution space) is

commensurate in size with the problem space, the problem space can be divided into sets of simpler

tasks, each requiring specialized competence, the simpler tasks can be dealt with autonomously by

individual agents, solving the overall situation requires cooperation among

the agents.

Take a descriptive approach!After all, SQL does it!State your objectives, let the system determine the best way how to achieve them!

We need software engineering for MAS.Disciplines that could help:

Model-driven programming Service-oriented programming Component-orientation

Agent platforms are available.

But then, agents and MAS should not be indeterministic beyond their own control, such as own disturbances!


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SOFSEM04

Mastering the complexity


proactive


interacting


proactive


proactive

No un-controlled disturb-ances!


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SOFSEM04

Transactions


proactive


interacting


proactive


proactive

Transactional agents

Transactional conversationsAgent may be involved in concurrent conversations!


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SOFSEM04

INTERRAP BDI agent architecture

World Model

Mental Model

Cooperation Model SG PS

SG PS

SG PS

World Interface

Agent knowledge base

Cooperative planning layer

Local planning layer

Behavior based layer

SG: Situation recognition and Goal activation PS: Planning, Scheduling and execution

information accesscontrol flow


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SOFSEM04

Corresponding transaction model

action node

control node

synchronization node

parallel execution

sequential execution

alternate execution

Open nested transactions

Recovery

Conversation


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SOFSEM04

Evolutionary transactions

World Model

Mental Model

Cooperation Model SG PS

SG PS

SG PS

World Interface

Agent knowledge base

Cooperative planning layer

Local planning layer

Behavior based layer

SG

...

PS

PS


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SOFSEM04

Agent cooperation by synchronization

agent 1 transaction

agent 2 transaction


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SOFSEM04

Independent start

Agent cooperation by synchronization

M1

M12M11 M13

M111 M121... M131

...

M1111...

M112

S1

S12

S122 S123S121

S11

S0

S2

Master

Slave

Task Delegation

M11 wakes up S11

M112 waits for S1 to regain control

prevents termination of slave before

termination of master

compensate in slave if M112 fails


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SOFSEM04

Experiments with an orthogonal architecture

domain agent

local actions

database

domain agent

local actions

transactionalagent

action execution

local TA tree

common goal

distributed plan

cooperation

coordination

action(DB transaction)

robustnessservice

transactionalagent

action execution

local TA tree

database

Isolation


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SOFSEM04

Not a nice solution because …

agent 1 transaction

agent 2 transaction

Need for a shared database for conflict resolution

Cooperation protocol directly built into individual agent transaction

Asynchronous communication via ECA rules that must be coordinated with database, e.g. via active database


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SOFSEM04

distributed transaction

Transactional conversations

agent 1 transaction

agent 2 transaction

conversation


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SOFSEM04

Modeling of conversations

readyinitiator participant cfpcfp_sentcfp

refuse

not-understood

propose

prop_refused

not_understood

prop_sent

abort

deal_reached

reject-proposal

accept-proposalprop_

acceptd

prop_refused

failure

inform-done

inform-ref

initiator

participant


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SOFSEM04

Transactional conversations

TA TaskTA Task TA TaskTA Task

Transaction manager (CORBA OTS)Transaction manager (CORBA OTS)

MAS Platform IMAS Platform I MAS Platform IIMAS Platform II

User Task

DB IDB I DB IIDB II

Agent AAgent A Agent BAgent B

User Task

FIPA conformant conversation

Drawback: Conversations must be attached to the

leaf nodes


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SOFSEM04

Message-oriented middleware (MOM) with messages based on

speech acts

The next idea

agent 1 transaction

agent 2 transaction

conversation


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SOFSEM04

Conclusions: Flexibility

MAS are very costly to engineer: System-level behavior is close to impossible to predict

analytically, requires large simulative or experimental effort. The added cost does not even pay off in the majority of

cases!

The expenses seem only justified for applications with large problem and solution spaces, where the problem space is or appears non-deterministic.

A natural application seems health care. Candidate technical applications are those with a high level

of disruptions.


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SOFSEM04

Conclusions: Flexibility

Approach: Transactional properties Requires an expensive and extensive infrastructure of

database manager and transaction manager. Heavy-weight nodes needed for agents. Distributed transactions (a foe of autonomy!) and active

database mechanisms (e.g., trigggers) needed.


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SOFSEM04

Challenges: Industrial strength

Transactional synchronization: Evolutionary transactions to deal with reaction. Transactions with built-in cooperation. Non-orthogonal individual and cooperative behavior.

Transactional conversations: Difficult to attach to agent transactions. Rigid, must have ACID properties.

Message-oriented middleware: Still an unknown area for agents. Long term: A combination of transactional agents and

message-oriented middleware with higher-level guarantees. Contribute to standards!


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SOFSEM04

Conversation Policy XML (cpXML)

• Origin:

• IBM (Conversation Support for Webservices)

• Status:

• V 1.0 (August 2002)

• Specification: XML Schema, Standards document, papers, tutorial

• Coverage:

• Sequencing and timing of messages

• Tools

• Reference implementation for WebSphere

• JCA extension by Conversation Manager and Conversation Adapter

• Remarks

• Compatible to BPEL4WS

• CPs exchangeable

Roles

ConversationPolicy

SendMessageTransition

TimeoutTransition

State InitialState

LoadChild

ChildReturnTransition

Role


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SOFSEM04

FIPA-Standard

Agent Communication•Ontology Service•ACL Message Structure

Interaction Protocols•IP Library Spec

•Request, Request-When•Query•Subscribe•Propose•Contract Net, Iterated ~•English, Dutch Auction•Brokering, Recruiting

Communicative Acts•CA Library Spec

Content Languages•Content Lang. Spec•SL, CCL, KIF, RDF

Agent Management•Agent Mgmt Spec

Agent Message Transport•Msg Transport Service•Messaging Interoperability

ACL Representations•ACL Msg Representation in

•Bit Efficient•String•XML

Envelope Representations•Agent Msg Transport Envelope Representation in

•XML•Bit Efficient

Transport Protocols•Agent Msg Transport Protocol for

•IIOP•WAP•HTTP

Applications•Nomadic Application Support•Agent Software Integration

•Message Buffering•QoS•Network Mgmt

•Personal Assistant, Personal Travel Assistant•Audio-visual Entertainment and Broadcasting

Abstract Architecture•Abstract Architecture•Domains and Policies

Foundation of

Interoperable Agents


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SOFSEM04

Augmenting FIPA

FIPA-OS

Agent Management

System„White Pages“

Robustness service

Standard system DWStandard system

Scheduling

Task Task

Data Warehouse

SoftwareTask

Standard system ...

Directory Facilitator

„Yellow Pages“ Task

Data Warehouse„Agent“

Order agent Machine agent

...

© 2004 ipd, prof. lockemann universität karlsruhe (th) sofsem04 flexibility through multiagent...

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