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expectations regarding observable behavior have been presented as a means for dynam-
ically structuring agent relationships, avoiding the necessity of a priori assumptions
regarding agent properties and behavior.
Based on theoretical foundations from sociology [11], a simulation approach to
emerging interaction patterns using expectations has been adapted and generalized to be
applicable in multiagent systems. That method has been evaluated in a supply network
scenario according to coordination efficiency and reliability as well as logistics per-
formance. The results illustrate that self-organized agent coordination based on mutual
expectations is able to establish organizational structures which allow for near opti-
mal performance rates regarding the evaluation criteria. Hence, the approach has been
shown to enable efficient interaction of autonomous entities to emerge solely based on
locally observable agent behavior.
However, there are still questions open for future examination. While the presented
approach performs very well in a stable agent community with repeating interaction
contents (i.e., a static supply network setup), it remains to be analyzed in a setting
with dynamically changing agent memberships and activities. In such a scenario, a
self-organizing network can be assumed to actually outperform a predefined structure
as the latter is not able to adapt to changing conditions. Furthermore, in that context,
an examination of the different parameters' impact on the predictability and speed
of convergence (learning rate) and the limits of overall performance of the emerging
system structure will give further insights into the capabilities of expectation-based
self-organization. This may motivate further refinements of that approach to agent
coordination.
Acknowledgements. This research is supported by the German Research Foundation
(DFG) within the Collaborative Research Center 637 “Autonomous Cooperating Logis-
tic Processes: A Paradigm Shift and its Limitations” (SFB 637) at Universitat Bremen,
Germany.
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