Adapting Environment-Mediated Self-Organizing Emergent Systems by Exception Rules Holger Kasinger, Bernhard Bauer, Jörg Denzinger and Tom Holvoet

Programming Distributed Systems Lab 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) June 7, Holger Kasinger

Programming Distributed Systems Lab Agenda  Introduction  Runtime insufficiencies  Efficiency Improvement Advisor  Exception rules  Conclusions June 7, Holger Kasinger

Programming Distributed Systems Lab Runtime insufficiencies  Case study: Dynamic Pickup and Delivery Problem (PDP) June 7, Holger Kasinger Task:

Programming Distributed Systems Lab Runtime insufficiencies  Pollination-inspired coordination (PIC) for solving PDPs June 7, Holger Kasinger Task: Allomone Synomone Pheromone

Programming Distributed Systems Lab Runtime insufficiencies  Field-based task assignment (FiTA) for solving PDPs June 7, Holger Kasinger

Programming Distributed Systems Lab Runtime insufficiencies  Insufficiency 1: Reactiveness of agents June 7, Holger Kasinger

Programming Distributed Systems Lab Runtime insufficiencies  Insufficiency 2: Greediness of agents June 7, Holger Kasinger

Programming Distributed Systems Lab Runtime insufficiencies  Insufficiency 3: Absence of global knowledge June 7, Holger Kasinger

Programming Distributed Systems Lab Runtime insufficiencies  Insufficiency 4: Inability to ‘look into future’ June 7, Holger Kasinger

Programming Distributed Systems Lab Agenda  Introduction  Runtime insufficiencies  Efficiency Improvement Advisor  Exception rules  Conclusions June 7, Holger Kasinger

Programming Distributed Systems Lab 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) June 7, Holger Kasinger

Programming Distributed Systems Lab Efficiency Improvement Advisor  Functional architecture June 7, Holger Kasinger Receive histories Transform histories Extract recurring tasks 3 Optimize solution 4 Derive advice 5 (A,1) (B,2) (C,2) (D,3) (A,1) (B,2) (C,3) (D,2) Send advice 6 Centralized feedback control loop MAS

Programming Distributed Systems Lab Agenda  Introduction  Runtime insufficiencies  Efficiency Improvement Advisor  Exception rules  Conclusions June 7, Holger Kasinger

Programming Distributed Systems Lab Exception rules  Classification June 7, Holger Kasinger Exception rules Task-triggered rules Ignore rules Boost rules Wait rules Time-triggered rules Forecast rules Detection rules Neighborhood- triggered rules Idle rules Path rules Event-condition-action rules

Programming Distributed Systems Lab Exception rules  Effect of ignore rules June 7, Holger Kasinger

Programming Distributed Systems Lab Exception rules  Experimental evaluation June 7, Holger Kasinger Unoptimized solutionOptimized solution

Programming Distributed Systems Lab Exception rules  Experimental results June 7, Holger Kasinger Random Scenarios Time Windows Changing Tasks 14% 17% Improvement

Programming Distributed Systems Lab Agenda  Introduction  Runtime insufficiencies  Efficiency Improvement Advisor  Exception rules  Conclusions June 7, Holger Kasinger

Programming Distributed Systems Lab 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 June 7, Holger Kasinger

Programming Distributed Systems Lab 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? June 7, Holger Kasinger

Programming Distributed Systems Lab Thank you for your attention! June 7, 2010Holger Kasinger22