Development of decision support system «BPsim.MSN»

Konstantin A. Aksyonov, Ph. D., IEEE member,Eugene A. Bykov, IEEE member

Elena F. Smoliy Maksim V. Popov

Leonid G. Dorosinskiy

Ural State Technical UniversityEkaterinburg, Russian Federation

10th PhD Workshop on Systems and ControlHluboka nad Vltavou, Czech Republic, 25 September 2009

Development of decision support system «BPsim.MSN»

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Multi-service telecommunication networks design and modeling application

Field of Interest

System analysis of business processesSimulation modelingMulti-agent systemsDecision making systemsComputer-aided software engineeringIntegration of simulation and CASE tools

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Outline

Theoretical aspectsMulti-agent approachDecision support over simulationDeployment experienceDemosConclusion

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BPsim products development background

Popular modeling tools comparisonCommon drawbacksAgent-based approach implementationRetail price

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Model of Resource Conversion Process

Transformation of resources

Controlled by agents

Operation in/out- puts

MechanizmsControl

commands and messages

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A1Sender 1 Receiver 1PR 1

Res1, Res2

Res6, Res7, Res8

U1, U2,U3, U4

P1, P2

Op2

Res1

Res2

Ca2 U1

Mech1 Mech2 Mech3

Res3

Res5

Message 1

Message 2

Mech5

Mech4

Res6

Res7

Res8

Op4

Ca4 U4

Op3

Ca3 U3

Junction 1

Ca_J1 U2

A2

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Resource Conversion Process Features

Structure and behavior algorithms complexity

Large amount of parameters

Complex hierarchyProbabilistic behaviorDecision-making

processes and decision-making people definition capability

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L=0 (maximum topological definiteness graph)

L=1

L=2

Process Operation Agent

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Agent Features

Control over RCP objectsEnvironment analysisSituation diagnosis and decision

search (interaction with a database)

System goals determination, delegation and achievement control

Message exchange (communication, negotiation, etc.)

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Agent Properties

Individual knowledge baseSet of goals for behavior

configuration settingPriority that defines agent order

in priority gaining queue

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Tool Application

Urals Industrial Group

Model designDefining agent

settingsMonitoring goals

achievement progress

Experiments output

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«Launch agent» knowledge base

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Intelligent reactive agent model

Frame-based approach Decision search diagramsFrame-concept (FC)

Frame name (FN)

Frame type (FT)

Application info (AI)

...

...

...

Behaviour scenarios structure (BSS)

Scenario selection block

Scenario 1

Scenario 2………

Slots structure (SLS)

Slot 1

Slot 2……

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Multi-service network model

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MULTI-AGENT SIMULATION

Demo

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Conclusions

Integration of Simulation and multi-agent approach Expert system Situational modeling

Result – intelligent decision support system, applied to multi-service network design

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Q/A

Thank you for your attention!

Questions?

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