Pro-active Advice to Improve the Efficiency of Self-Organizing Emergent Systems

Steiner, Th.; Denzinger, Jörg; Kasinger, Holger; Bauer, Bernhard

doi:10.1109/ease.2011.11

Cited by 5 publications

(11 citation statements)

References 9 publications

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“…Experimental results in [27] demonstrated both the high potential and corresponding risk that exception rules represent. Therefore, it is desirable that (1) the risks associated with rules are assessed before communication to agents, (2) the frequency of interaction is decreased, and (3) a minimal comprehensive set of rules is provided.…”

Section: B Eia Weaknessesmentioning

confidence: 91%

“…In contrast pro-active exception rules, described in [27], influence an agent to begin work on a particular abstract task (defined within variability allowed by sim), before this task usually appears. The purpose of this variant is twofold.…”

Section: A Eia Exception Rule Derivationmentioning

confidence: 98%

“…Similar to previous work in [26], [13], and [27], we use pickup and delivery problems (PDP) [23] as the application domain for the experimental evaluation of this concept. Our results show that the RA-EIA, by taking advantage of the time available through independent offline execution, is able to provide minimal risk-averse advice achieving improved performance compared to the original EIA.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, [16] calls attention to unaddressed operational inefficiencies in self-organizing systems where ignore rules were ineffective. To address this, in [27], the concept of proactive rules, that suggest to an agent to start working on the first step of an expected forthcoming recurring task after the occurrence of a trigger, was introduced. Results demonstrated that these pro-active rules successfully improved efficiency when ignore rules were ineffective, but were easier to exploit as they represent a more invasive influence on flexibility.…”

Section: Introductionmentioning

confidence: 99%

“…Results demonstrated that these pro-active rules successfully improved efficiency when ignore rules were ineffective, but were easier to exploit as they represent a more invasive influence on flexibility. The results in [27], using the automated testing method introduced in [13], exposed the danger that the good-intentioned but short-sighted application of rules by the original EIA can negatively impact the dependability of the system.…”

Section: Introductionmentioning

confidence: 99%

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Dependable Risk-Aware Efficiency Improvement for Self-Organizing Emergent Systems

Hudson

Denzinger

Kasinger

et al. 2011

2011 IEEE Fifth International Conference on Self-Adaptive and Self-Organizing Systems

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An efficiency improvement advisor agent acts as a consultation service for a self-organizing multi-agent system that improves operational efficiency. It identifies recurrent tasks in past problems that allow the creation of so-called exception rules for individual agents to limit future inefficient behavior. There exists the danger that introduced rules could possibly infringe on the flexibility and therefore reliability of the system. In this paper, we present a dependable riskaware efficiency improvement advisor that uses Monte Carlo simulation techniques in strategic analysis assessing the longterm potential and risks of prospective rules. Our experimental evaluation, for the domain of dynamic pickup and delivery problems, shows that the result is a minimal, yet effective, set of risk-averse exception rules. These rules can be provided to individual agents to reliably achieve an overall long-term improvement in efficiency while maintaining flexibility.

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Section: B Eia Weaknessesmentioning

confidence: 91%

Section: A Eia Exception Rule Derivationmentioning

confidence: 98%