slide1 n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
On the Modeling, Refinement and Integration of Decentralized Agent Coordination PowerPoint Presentation
Download Presentation
On the Modeling, Refinement and Integration of Decentralized Agent Coordination

Loading in 2 Seconds...

play fullscreen
1 / 36

On the Modeling, Refinement and Integration of Decentralized Agent Coordination - PowerPoint PPT Presentation


  • 87 Views
  • Uploaded on

On the Modeling, Refinement and Integration of Decentralized Agent Coordination – A Case Study on Dissemination Processes in Networks. International Workshop on Self-Organizing Architectures (SOAR 09) Cambridge, UK. 2009-03-25. Distributed Systems Architectures. Challenge:

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'On the Modeling, Refinement and Integration of Decentralized Agent Coordination' - nikita


Download Now 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
slide1

On the

Modeling, Refinement and Integration

of Decentralized Agent Coordination

– A Case Study on

Dissemination Processes in Networks

International Workshop on Self-Organizing Architectures (SOAR 09)

Cambridge, UK

2009-03-25

distributed systems architectures
Distributed Systems Architectures
  • Challenge:
    • Building adaptive applications that are scalable, robust, …
  • Architectural Choices:
  • Managed Hierarchical Decentral

Pyramid of

Managing

Entities

Managing

Entity

Scalability,

Robustness, …

Here:

Utilization of

Self-Organizing

Processes

  • Local adaptive entities: software agents
  • Problematic: effective coordination
self organization as a software design principle
Self-Organization as a (Software) Design Principle
  • Self-Organization:
    • physical, biological and social phenomena,
    • global structures arise from the local interactions of autonomous individuals (e.g. particles, cells, agents, ...)
    • Structures are:
      • Adapted to changing environments
      • Maintained while being subject to perturbations
  • Attractive for software architects:
    • Decentralized coordination strategies / mechanisms
      • No single point of failure
      • Conceive application dynamics  resemble phenomena
      • Blending of functionality and coordination aspects (Reuse, Redesign)
  • Requirement: Systematic conception / integration
    • Declarative configuration of agent coordination
    • Enactment architecture

(Sudeikat & Renz 2008, 2009)

proposal programming model for self organization
Proposal: Programming Model for Self-Organization
  • Self-organizing processes result from coupled feedbacks between system elements
    • Context dependent amplification / damping of element activities
  • Systemic Modeling Approach
    • System Science concepts characterize MAS operation
      • System Variables: # behavior exhibitions (roles, groups, …)
      • Causal Relationships: rates of variable changes
      •  Feedback-Networks
  • Toolset:
    • Configuration Language
    • Enactment Architecture

reinforcing

/ balancing

+

-

+

+

+

(-)

(+)

+

+

+

+

coordination enactment architecture
CoordinationEnactmentArchitecture

Layered Approach

Application

Coordination

Coordination Media

Interaction techniques

Agent-Modules

Execution Infrastructure

Coordination-Endpoint: Agent-modules

Interface Coordination Media

Publish / Subscribe mechanism

Automatingcoordination-activities

1: Agent observation / modification

2: Controlledbycoordination model

3: Publicationofagentadjustments

Externalized Coordination

Model

coordination enactment architecture1
CoordinationEnactmentArchitecture

Coordination-Endpoint:

  • Agent State Interpreter
    • Observeagentexecution
    • Behavior-Classification
    • Behavior-Change Publication
  • Coordination Information

Interpreter

    • Reception via CM.
    • Adjustmentofagent-behavior

LocalAdaptivity:

  • Declarative: Conditions / Invariants
  • AdaptivityComponent: (optional)

 ProceduralImplementationof

    • ClassificationofObservations
    • Adaptationsof Agent state

Coordination Medium

  • Publish / Subscribe Interface

Realizingself-organizingprocesses:

Information Flows

Local Element Adaptivity

methodic conception of so processes
Methodic Conception of SO-Processes
  • Integration of Coordination Development in AOSE
      • AOSE: Tools / techniques for agent development
      • Plan for concerted phenomena
  • Systematic refinement procedure

Describing System Behavior

    • Identify Problem Dynamic
      • Structures
      • Attractors
      •  coupled feedback loops
    • Propose Solution Dynamic
      • Opposing / Corrective Structure
    • Refinement operations
      • Map Coordination model to Agent models
case study i convention emergence
Case Study I: Convention Emergence
  • Decentralized agreement problem in MAS
    • Communication of local settings
    • Agents adjust accordingly
  • Embedding an externalized Coordination Model
    • Generic agent activity
    • Coordination Model:
      • Observation of activities
      • Communication of configurations
      •  Adjustment

Policy: majority rule

    • +/- feedback loop
    • Coordination Medium: Overlay-Network Topology

Convergence

case study i convention emergence1
Case Study I: Convention Emergence
  • Sample Simulation Run:
    • Random Initialization
    • Value Convergence
      • Random agent activation
      • Communication:

Coordination Medium

  • Impact of Network-Topology:
    • Random Graph
    • Power law Graph:
    • Comparable convergence times
      • Less communicative overhead in power law distributed graphs
case study ii patching dynamics
Case Study II: Patching Dynamics
  • Exemplify refinement process:
  • Problem description  correcting coordination process
  • Problem:
    • Spreading of “infections” in agent population
    • Agent exhibit two Roles:
      • Susceptible
      • Infectious
    • Balancing vs. reinforcing Feedback  Goal-Seeking
  • Possible Solution Dynamic:
    • Additional Balancing Feedback
    • Limit Susceptible and Infectious agents
case study ii patching dynamics1
Case Study II: Patching Dynamics
  • Refined Solution Dynamic
    • Executable!
    • Adaptivity Component
      • Functionality
      • Behavior Classification
    • Information Flow
  • Sample Simulation Run
    • One random infection
    • Fixed infection rate
      •  Epidemic
    • Recovery of initial infection starts recovering process

infected

unsusceptible

conclusions i
Conclusions I

Embedding of self-organizing processes in MAS

  • Architectural Aspect:
    • Proposal:
      • Reference Architecture
      • Declarative language support
    • Supplement Coordination
    • Encapsulation of:
      • Adaptation logic
      • Information Flow / Interaction Technique
  • Methodic Aspect:
    • Equip self-organizing process to correct / oppose problematic dynamics
conclusions ii
Conclusions II

“… how their contribution connects the self‐adaptive

perspective with the self‐organizing perspective”

  • (System) Self-Adaptivity by concerted entity adaptivity
    • Adaptive Software System:
      • Establishment of closed feedback loop, e.g. MAPE, …
    • Here:
      • Collective adjustments of individual elements
      • Closed feedback is distributed among system elements

Sets of feedback loops

System coordination model

slide14
End

Thank you for your Attention!

Questions / Suggestions are welcome!

case study i convention emergence2
Case Study I: Convention Emergence
  • Sample Simulation Run:
    • Random Initialization
    • Value Convergence
      • Random agent activation
      • Communication:

Coordination Medium

  • Impact of Network-Topology:
    • Random Graph
    • Power law Graph:
    • Comparable convergence times
      • Less communicative overhead in power law distributed graphs
encapsulating adaptivity interaction
Encapsulating Adaptivity / Interaction

Foundationalelementsof a self-organizingprocesses

Information FlowsLocal Element Adaptivity

  • Coordination Media:
    • Information exchangetechniques
      • Tuplespace, spatialenvironments,…
    • Here, Overlay-Network
      • Topologyconstraintscommunication
  • Coordination Endpoints:
    • Localadpatationknowledge
    • Automation of coordination-related activities
exemplifying systemic modeling of mas
Exemplifying Systemic Modeling of MAS
  • Systemic Modeling
    • Causal relations of system variables
      • Describe Entity behaviors
    • Anticipation of the

Qualitative System Dynamics

      • Manual inspection

and/ or simulation

  • A Hypothetical System:
    • Producers  Products
    • Products  Storage
    • Storage  Production

Balancing

Feedback

Practical development:

After a suitable causal structure has been found:

How to implement ?

masdynamics declaration of agent behavior interdependencies
MASDynamics: Declaration of Agent Behavior Interdependencies
  • Systemic system model:
    • Nodes  System Variables
      • # of role occupations
      • # of groups
    • Interdependencies: Links
      • Direct:
        • e.g. service invocations, …
      • Mediated:
        • using environment models, e.g. pheromones, tuple spaces, …
    • Description levels:
      • Application independent
      • Alignment with agent implementation:

Node Types

Link

Types

  • Nodes:
    • Referencing reasoning events
    • that indicate behavior adjustments,
    • E.g. goal adoptions, plan activations, …
  • Links:
    • Configuring interaction techniques
    • E.g. environment models, …
coordination strategies
Coordination Strategies
  • Systemic Modeling of macroscopic dynamics
    • Compensating
    • Amplifying
    • Selective
coordination strategies1
Coordination Strategies
  • Systemic Modeling of macroscopic dynamics
    • Compensating:
coordination strategies2
Coordination Strategies
  • Systemic Modeling of macroscopic dynamics
    • Amplifying:
coordination strategies3
Coordination Strategies
  • Systemic Modeling of macroscopic dynamics
    • Selective:
decentralized coordination mechanisms
Decentralized Coordination Mechanisms
  • Information Exchange techniques
  • Classification:
expressing coordination dynamics
Expressing Coordination Dynamics
  • Structural Properties of SO-Systems
    • Positive Feedback
      • Amplification of appropriate entity activities
    • Negative Feedback
      • Damping inappropriate entity activities
    • ...
  • Dynamic Viewpoint on application development:
    • Consider dyn. properties at design-time
    • Design the causes of self-organization
  • MAS specific modelling level:
    • Agent-based design concepts:
      • Roles: Abstraction of agent behaviours
      • Groups: sets of individuals that share common characteristics (e.g.: collective goals)
      • System State:
        • # of behaviour occupations
case study decentral web service management
Case Study: Decentral Web-Service Management
  • Agent-based Web-Service Management Architecture
    • Balance service workloads
  • Management Agents:
    • (J2EE) Service-Endpoint
    • Broker Agents
      • Registries: Service-Endpoints
  • Prototype Implementation:
    • Jadex Agent Platform
      • Cognitive agent model  Beliefs, Goals, Plans, Internal Events, …
    • SUN Appserver Management Extensions (AMX)
      • Server-Management Interface

Conceptual Architecture

http://jadex.informatik.uni-hamburg.de/bin/view/About/Overview

https://glassfish.dev.java.net/javaee5/amx/

case study decentralized web service management
Case Study: Decentralized Web-Service Management
  • A Functional, but un-coordinated Implementation
    • Manual management of is enabled
    • Tropos Modeling Notation
    • Dependencies of agent types
      • Client  Service Endpoint
      • Client  Broker
      • Broker  Service Endpoint
      • Broker  Client
  • Systemic Description of the Causal Application structure
    • Accumulative system variables
  • Complementing the causalities
    • Establish a negative feedback loop
      • Agent state definitions
      • Establishment of interdependencies

Tropos Design Notation

case study decentralized web service management1
Case Study: Decentralized Web-Service Management
  • Embedding Coordination:
    • Strategy Definition:
      • Variable / Link Declarations
    • Strategy alignment / integration
      • Referencing agent models
      • Configuring interaction technique
  • Validation:
    • Provoking the manifestation of the feedback loop
      • Responsive regime
      • Sudden demand for specific service type

Event Publications

Event Perceptions

Middleware Configuration

case study behavioral analysis by applying stochastic process algebra
Case Study: Behavioral Analysis by Applying Stochastic Process Algebra
  • Stochastic Process Algebra:
    • Behavioral modeling
    • System of interacting processes
    • Coupled by synchronized activities
  • Validation of qualitative dynamic:
    • Provoking the effects of the feedback loop
      • Responsive regime
      • Initial Conf.:
        • Allocation of service 1
      • Input:
        • High demand of service 2

Balance of allocations

mesoscopic modeling
Mesoscopic Modeling
  • Available formalisms:
    • Macroscopic System
      • System Sciences
      • Mathematics, …
    • Microscopic System
      • Local entity (inter-)actions
      • State Machines, Process Algebra, …
  • Transition:
    • Simulation / Iteration of microscopic models
  • Proposal: (Renz & Sudeikat, 2005, 2006)
    • Intermediate description levels:
    • Mesoscopic agent states
    • Classification of agent behaviors
      • Relevance of agent activities with respect to the Macroscopic System Behavior
      • Abstraction of the microsopic agent activities
  • Mesoscopic agent states:
  • Not microscopic:
    • Coarse grained agent activities
  • Not macroscopic:
    • Exhibits short time fluctuations
applying mesoscopic modeling
Applying Mesoscopic Modeling

Top-Down:

  • E.g.: MASDynamics
    • Transfer of System Dynamics concepts
    • Graph-based modeling

Bottom-up:

  • E.g.: Stochastic

Situational Calculus

    • Extension of the Sit. Calculus
  • Two orthogonal approaches:
    • Different modeling directions
    • Enabling iterative development:
      • Explain rising phenomena
      • Tune rising phenomena
  • coarse-graining element
  • dynamics
  • inferring collective
  • system properties
  • modeling macroscopic
  • dynamics
  • refinement to
  • intermediate scales
top down systemic mas modeling
Top-Down: Systemic MAS Modeling
  • MAS abstraction by:
    • Agent-based design concepts:
      • Roles: Abstraction of agent behaviours
      • Groups: sets of individuals that share common characteristics (e.g.: collective goals)
      • Global MAS State:
        • # of behaviour occupations
  • Graph Definition:
    • Nodes: System Variables
      • # of role occupations
      • # of organizational groups
      • size of organizational groups
      • quantification of environment

elements ( #, size, etc. )

    • Links: Causal relations
        • Environment mediated
        • Direct agent interactions

MAS Design

  • Modelling the
  • causes of
  • Self-organization:
  • Feedback Loop
  • Structures
top down systemic mas modelling
Top-Down: Systemic MAS Modelling
  • Allows for model refinement
    • Attachment: add detail
    • Link: detail link dynamics
    • Variable: detail variable intern dynamics
  • Example: Ant-based path finding

(-)

(+)