Uml for behavior oriented multi agent simulations
This presentation is the property of its rightful owner.
Sponsored Links
1 / 14

UML for Behavior-Oriented Multi-Agent Simulations PowerPoint PPT Presentation


  • 65 Views
  • Uploaded on
  • Presentation posted in: General

UML for Behavior-Oriented Multi-Agent Simulations. Christoph Oechslein , Franziska Klügl, Rainer Herrler, and Frank Puppe University Würzburg, Germany. UML for Behavior-Oriented Multi-Agent Simulations. Christoph Oechslein , Franziska Klügl, Rainer Herrler, and Frank Puppe

Download Presentation

UML for Behavior-Oriented Multi-Agent Simulations

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Uml for behavior oriented multi agent simulations

UML for Behavior-Oriented Multi-Agent Simulations

Christoph Oechslein, Franziska Klügl, Rainer Herrler, and Frank Puppe

University Würzburg, Germany


Uml for behavior oriented multi agent simulations1

UML for Behavior-Oriented Multi-Agent Simulations

Christoph Oechslein, Franziska Klügl, Rainer Herrler, and Frank Puppe

University Würzburg, Germany


Uml for behavior oriented multi agent simulations2

UML for Behavior-Oriented Multi-Agent Simulations

  • Motivation

  • UML

  • Behavior-Oriented Multi-Agent Simulation (bMASim)

  • Using UML for bMAS

  • Small Example

  • Conclusion & Future Work

Christoph Oechslein, Franziska Klügl, Rainer Herrler, and Frank Puppe

University Würzburg, Germany


Motivation

Motivation

  • Model concept to running experiment non trivial:

    • Top-down analysis  bottom-up implementation

    • Concurrent (inter)actions

    • Complex agent behavior leads to large models

    • Huge amount of parameters

  • Own non standard specification language

  • Need for tools and frameworks which

    • Are easy to learn, clear and understandable and

    • Solve the above problems, e.g. scale for concrete domains.

CEEMAS 2001 - UML for bMAS


Uml for behavior oriented multi agent simulations

UML

  • Already established framework in software engineering.

  • Diagram types for static and dynamic parts(e.g. activity graph and sequence diagram).

  • Includes OCL a formal language for attaching additional information, like invariants, constraints, etc..

  • Meets criteria for ‘ideal’ framework

CEEMAS 2001 - UML for bMAS


Behavior oriented multi agent simulation bmasim

Behavior-OrientedMulti-Agent Simulation (bMASim)

  • Focus not on the internal reasoning process,but on the agent behavior and its interactions.

  • Associate task fulfillment with activities.

  • Incorporating organizational concepts like roles.

  • Includes a simulated environment.

  • Agent entails state variables and a rule-based behavior representation (based on activities).

CEEMAS 2001 - UML for bMAS


Using uml for bmasim

Using UML for bMASim


Class diagrams

+ deposit_trail (Pheromone)

+ following_trail (Pheromone)

+ forage (Environment)

– feed (Ant)

Class Diagrams

  • Shows state variables:

    • Type

    • Visibility attribute(private or public)

    • Constant

  • Interaction methods:

    • Visibility attribute(private or public)

    • Interaction partner

Ant

– energy_level: double

+ const size: double

CEEMAS 2001 - UML for bMAS


Ocl in class diagrams

Ant

Nest

– energy_level: double

+ ants: List of Ants

+ constsize: double

+ preys: List of Prey

+ deposit_trail (Pheromone)

+ following_trail (Pheromone)

+ forage (Environment)

energy_level > 0

ants.count > 0

Ant.allInstances->sum(energy_level) < MAX_VALUE

ants.sum(size) + preys.sum(size) < MAX_SIZE

global invariants

local invariants

OCL in Class Diagrams

  • Class invariants: local and global

CEEMAS 2001 - UML for bMAS


Activity graphs for bmas

Activity Graphs for bMAS

  • Describing the behavior of an agent as an activity graph

  • State-like activities also well suited

  • Transitions are conditions on external and internal state variables

  • Example

    Missing:Describing the interactions

CEEMAS 2001 - UML for bMAS


Interaction types

...

Larva behavior

Adult behavior

  • Interaction via Agent Generation

interaction via

...

creating larva

creating larva

transform

...

interaction via

creating adult

...

  • Interactionvia Variable Modification

Ant behavior

Nest

interaction via

variable food

+ food: double

forage

in nest

...

Interaction Types

  • Interaction via Object Flow

Ant behavior

...

...

Pheromone

follow trail

deposit trail

...

...

CEEMAS 2001 - UML for bMAS


Predator prey example

Prey_Class

Predator_class

Energy : number

Energy

: number

Reproduction_Cost (RC)

Absorbable_Energy_from_food (abE)

Reproduction_Cost (

RC

)

Energy_boundary (E

)

Absorbable_Energy_from_prey

bound

(

abE

)

Success_probability (

prob

)

Predator takes all

succ

Energy <

abE

Energy_boundary (

E

)

energy

of prey

bound

instance

Generate new predator

Generate new

instance

prey instance

with

Energy

:=

with

Energy

:=

StartEnergy

StartEnergy

Food_Class

Prey acquires

energy

of

Energy :

number

food instance, no change

in food instance

‘Predator – Prey’ Example

Perishing

Perishing

Succumb

always

energy := 0

Energy <

abE

same position as

successful predator

Searching

random movement

Searching

Ù

random movement

Ù

RC

- with

always

same position as prey

always

same position as predator

RC

Energy >

prob

same position as

always

same position as prey

food instance

Energy >

always

succ

Reproduce

Feeding

Reproduce

Capure Prey

energy := energy +

energy = energy +

generate new predator

generate new prey instance

Food.Energy *abE

prey.energy

*

abE

instance

energie

=

energie

-

RC

energie

=

energie

-

RC

CEEMAS 2001 - UML for bMAS


Ocl in activities or states

OCL in activities or states

  • Pre- and post conditions:

    • in activities or states

    • describing interactions

Pick up Prey

precondition:

jawfull != true

post condition

jawfull = = true

  • OCL for adding precise and unambiguous information

    • basis for validation and verification

    • lower the gap between implementation and design

CEEMAS 2001 - UML for bMAS


Conclusion and future work

Conclusion and Future Work

  • Using various parts of UML for bMASim:

    • Activity graphs prominent mean

    • Describing interactions in activity graph

    • Using OCL for adding precise and unambiguous information

  • Classification of interaction types

  • Future work:

    • Structuring the information in documentation nodes

    • Methodology from concept model to UML

    • Application of this framework in real world models

CEEMAS 2001 - UML for bMAS


  • Login