On the Modeling, Refinement and Integration of Decentralized Agent Coordination

1. 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

2. 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

3. 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)

4. 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 + - + + + (-) (+) + + + +

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. End Thank you for your Attention! Questions / Suggestions are welcome!

15. 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

16. 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

17. Coordination Pattern

18. Systemic Software Modeling

19. Modeling Notation

20. 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 ?

21. 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, …

22. Coordination Strategies • Systemic Modeling of macroscopic dynamics • Compensating • Amplifying • Selective

23. Coordination Strategies • Systemic Modeling of macroscopic dynamics • Compensating:

24. Coordination Strategies • Systemic Modeling of macroscopic dynamics • Amplifying:

25. Coordination Strategies • Systemic Modeling of macroscopic dynamics • Selective:

26. Decentralized Coordination Mechanisms • Information Exchange techniques • Classification:

27. 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

28. 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/

29. 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

30. 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

31. 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

32. 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

33. 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

34. 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

35. 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 (-) (+)

36. Self-Organization vs. Emergence • Methodological view