adapting environment-mediated self-organizing emergent systems by exception rules holger kasinger,...

Adapting Environment-MediatedSelf-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, 2010 2Holger Kasinger


Agenda

Introduction Runtime insufficiencies Efficiency Improvement Advisor Exception rules Conclusions



Runtime insufficiencies

Case study: Dynamic Pickup and Delivery Problem (PDP)


Task:



Pollination-inspired coordination (PIC) for solving PDPs


Task: Allomone

Synomone

Pheromone



Field-based task assignment (FiTA) for solving PDPs




Insufficiency 1: Reactiveness of agents




Insufficiency 2: Greediness of agents




Insufficiency 3: Absence of global knowledge




Insufficiency 4: Inability to ‘look into future’



Agenda




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)



Efficiency Improvement Advisor

Functional architecture


Receive histories0110100111101

1

Transform histories0110100111101

2

Extract recurring tasks3 Optimize solution4

Derive advice5 (A,1)(B,2)(C,2)(D,3)

(A,1)(B,2)(C,3)(D,2)

Send advice6

Centralizedfeedback

control loop

MAS


Agenda




Exception rules

Classification


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


Exception rules

Effect of ignore rules



Exception rules

Experimental evaluation


Unoptimized solution Optimized solution


Exception rules

Experimental results


RandomScenarios

TimeWindows

ChangingTasks

14%

17% 17%

Impr

ovem

ent


Agenda




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



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?



Thank you for your attention!

June 7, 2010 Holger Kasinger 22

adapting environment-mediated self-organizing emergent systems by exception rules holger kasinger,...

Documents

holger kasinger slide

holger kasinger task

tom holvoet slide

reactiveness of agents

greediness of agents

agents decision

transform histories

adapting environment