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Published in:
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2016 | OriginalPaper | Chapter

Continuous Collaboration for Changing Environments

Authors : Matthias Hölzl, Thomas Gabor

Published in: Transactions on Foundations for Mastering Change I

Publisher: Springer International Publishing

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Abstract

Collective autonomic systems (CAS) are distributed collections of agents that collaborate to achieve the system’s goals but autonomously adapt their behavior. We present the teacher/student architecture for locally coordinated distributed learning and show that in certain scenarios the performance of a swarm using teacher/student learning can be significantly better than that of agents learning individually. Teacher/student learning serves as foundation for the continuous collaboration (CC) development approach. We introduce CC, relate it to the EDLC, a life cycle model for CAS, and show that CC embodies many of the principles proposed for developing CAS.

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previous chapter Collective Autonomic Systems: Towards Engineering Principles and Their Foundations
next chapter Issues on Software Quality Models for Mastering Change
Footnotes
1
Taking into account the different times at which students exchange information with teachers, the knowledge and strategies the students share are typically similar, not identical. This does not change the gist of the following discussion.
 
2
The careful reader may observe that the single robot takes only approximately 6 times as long as the swarm to reach its maximal performance, not more than 10 times as might be expected. This is an artifact of our learning schedule which learns only at the end of each episode, so that the single agent performs many more iterations of the DP algorithm before it reaches its maximum performance than the DP-learner and thus better exploits the data it has available. This means that the single agent can focus a larger percentage of its exploration on promising parts of the graph, thereby negating the advantages that the swarm has over a single learner. However, a swarm of 10 single learners would use 10 times the computational resources of a swarm with a DP-learner, which would justify running the DP-learner 10 times as frequently with corresponding improvements to the swarm’s performance.
 
3
Between episodes 30 and 50 the random modifications result in a graph in which some of the routes computed by the non-learning teachers are viable, therefore the performance is slightly better than in the other episodes in which the graph is damaged.
 
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Metadata
Title
Continuous Collaboration for Changing Environments
Authors
Matthias Hölzl
Thomas Gabor
Copyright Year
2016
Book
Transactions on Foundations for Mastering Change I

Print ISBN: 978-3-319-46507-4

Electronic ISBN: 978-3-319-46508-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46508-1_11

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