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Adapting Environment-Mediated Self-Organizing Emergent Systems by Exception Rules

Adapting Environment-Mediated Self-Organizing Emergent Systems by Exception Rules. Holger Kasinger , Bernhard Bauer, Jörg Denzinger and Tom Holvoet. Introduction. Environment-mediated self-organizing emergent systems Many, simple elements (mostly realized by agents)

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Adapting Environment-Mediated Self-Organizing Emergent Systems by Exception Rules

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  1. Adapting Environment-MediatedSelf-Organizing Emergent Systemsby Exception Rules Holger Kasinger, Bernhard Bauer, Jörg Denzinger and Tom Holvoet

  2. Introduction • Environment-mediated self-organizing emergent systems • Many, simple elements (mostly realized by agents) • Decision making solely based on locally available information • Local actions and interactions achieve global system goals • Usage of decentralized coordination mechanism • Pheromone-based coordination • Infochemical-based coordination • Field-based coordination • Potential risk • Efficiency (performance) during operation cannot be guaranteed due to several runtime insufficiencies (system characteristics) Holger Kasinger

  3. Agenda • Introduction • Runtime insufficiencies • Efficiency Improvement Advisor • Exception rules • Conclusions Holger Kasinger

  4. Runtime insufficiencies • Case study: Dynamic Pickup and Delivery Problem (PDP) Task: Holger Kasinger

  5. Runtime insufficiencies Synomone Pheromone Allomone • Pollination-inspired coordination (PIC) for solving PDPs Task: Holger Kasinger

  6. Runtime insufficiencies • Field-based task assignment (FiTA) for solving PDPs Holger Kasinger

  7. Runtime insufficiencies • Insufficiency 1: Reactiveness of agents Holger Kasinger

  8. Runtime insufficiencies • Insufficiency 2: Greediness of agents Holger Kasinger

  9. Runtime insufficiencies • Insufficiency 3: Absence of global knowledge Holger Kasinger

  10. Runtime insufficiencies • Insufficiency 4: Inability to ‘look into future’ Holger Kasinger

  11. Agenda • Introduction • Runtime insufficiencies • Efficiency Improvement Advisor • Exception rules • Conclusions Holger Kasinger

  12. Efficiency Improvement Advisor • Specific constraints for feedback control loops • Low observability and poor controllability • Capability for self-organization and emergence • Openness and autonomy • Assumptions and premises • Each agent is able to collect data about its local behavior • Each agent can be extended to a rule-applying agent • A sequence of runs (days) must have a (sub)set of similar tasks in (nearly) each run (day) Holger Kasinger

  13. Efficiency Improvement Advisor • Functional architecture Extract recurring tasks Optimize solution 3 4 Transform histories Derive advice 2 5 (A,1) (B,2) (C,2) (D,3) (A,1) (B,2) (C,3) (D,2) 0110100111101 Centralized feedback control loop Receive histories Send advice 1 6 0110100111101 MAS Holger Kasinger

  14. Agenda • Introduction • Runtime insufficiencies • Efficiency Improvement Advisor • Exception rules • Conclusions Holger Kasinger

  15. Exception rules • Classification Event-condition-action rules Holger Kasinger

  16. Exception rules • Effect of ignore rules Holger Kasinger

  17. Exception rules • Experimental evaluation Unoptimized solution Optimized solution Holger Kasinger

  18. Exception rules • Experimental results 17% 17% Improvement 14% Random Scenarios Time Windows Changing Tasks Holger Kasinger

  19. Agenda • Introduction • Runtime insufficiencies • Efficiency Improvement Advisor • Exception rules • Conclusions Holger Kasinger

  20. Conclusions • Conclusions • EIA and exception rules • Adapt the local behavior of single agents in self-organizing systems • Improve the efficiency of the global solution • Take into account specific system constraints • More than just parameter adaptation • Current state • Done: ignore rules • In progress: boost rules, forecast rules • To be done: wait rules, detection rules, idle rules, path rules • Limitations • Not appropriate for problems without recurring tasks • Still limited in the size of problems Holger Kasinger

  21. Conclusions • Open questions • How to guarantee that the adaptation of the local behavior is not counterproductive and possibly worsens the global solution in awkward situations? • Will scalability in terms of millions of agents be an issue for real-world application domains? • What is the trade-off for decentralizing the EIA approach in terms of additional communication and coordination efforts? • Assumed that an optimal solution to a problem can be calculated, how close can we get to this solution by an adapted self-organizing emergent system? Holger Kasinger

  22. Thank you for your attention! Holger Kasinger

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