Adaptivity at every layer

Richert, Willi; Kleinjohann, Bernd

doi:10.1145/1370018.1370039

Proceedings of the 2008 International Workshop on Software Engineering for Adaptive and Self-Managing Systems 2008

DOI: 10.1145/1370018.1370039

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Adaptivity at every layer

Willi Richert

Bernd Kleinjohann

Abstract: We describe a developmental architecture that enables individual robots to fulfill tasks assigned to the robot society in a robust, decentralized manner. The architecture is meant to show emergent properties according to Organic Computing principles that are positive for the society's robustness and performance. This requires the architecture to feature those adaptation and learning processes that are not only selfishly useful for the individual robot, but also incorporate the robot society's actual needs at a… Show more

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Cited by 6 publications

References 17 publications

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Integrating sporadic imitation in Reinforcement Learning robots

Richert

Scheller

Koch

et al. 2009

2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning

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Although the combination of Reinforcement Learning and imitation has been already considered in recent research, it always revolved around fixed settings where demonstrator and imitator are fixed and the imitation process is a welldefined period of time. What is missing is the investigation of approaches that also work in scenarios where imitation is only sporadically possible. This means that in a multi-robot scenario a robot is now allowed to interrupt another robot by asking to repeat certain actions, but can only observe and integrate information bits delivered occasionally. In this paper we present how that can be done in continuous and noisy environment within an SMDP context.

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Integrating sporadic imitation in Reinforcement Learning robots

Richert

Scheller

Koch

et al. 2009

2009 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning

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Increasing the Autonomy of Mobile Robots by Imitation in Multi-robot Scenarios

Richert

Scheller

Koch

et al. 2009

2009 Fifth International Conference on Autonomic and Autonomous Systems

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Because imitation has shown to be able to drastically cut down the exploration space it has been embraced largely by the robotics community to speed up autonomous learning. This has been done mostly in fixed demonstratorimitator relationships, where a predefined demonstrator is performing the same action over and over again until the robot has learned it sufficiently. In practical multi-robot scenarios, however, the imitation process should not interrupt the observed robot as it will affect its autonomy. Therefore the imitating robot often has only a limited amount of behaviors of the same type to learn from. As this usually does not provide enough information for learning a generalized version of observed lowlevel actions, it can help the observer to optimize its strategy based on the recognized actions. In this case, first of all the imitating robot has to interpret the observed behavior in terms of its own behavior knowledge. This means that low-level observations have to be segmented into episodes of apparently similar behavior. For these episodes corresponding skills in the observing robot's own behavior repertoire have to be found. And for the episodes sequence corresponding state changes in the observing robot's own strategy have to be determined. How the former can be done has been shown by the authors in a previous paper. In this paper we will describe how our approach for Evolving Societies of Learning Autonomous Systems (ESLAS) is extended to be capable of multi-robot imitation. We demonstrate how the recognition results can be used in a multi-robot scenario to decentrally align the robots' behaviors, to speed up the overall learning process, and thus increase the overall autonomy.

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A test bed for investigating self-x properties in multi-robot societies

Jungmann

Kleinjohann

Lutterbeck

et al. 2011

2011 9th IEEE International Conference on Industrial Informatics

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Adaptivity at every layer

Cited by 6 publications

References 17 publications

Integrating sporadic imitation in Reinforcement Learning robots

Integrating sporadic imitation in Reinforcement Learning robots

Increasing the Autonomy of Mobile Robots by Imitation in Multi-robot Scenarios

A test bed for investigating self-x properties in multi-robot societies

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