Analyzing the Effects of Role Configuration in Logistics Processes using Multiagent-Based Simulation: An Interdisciplinary Approach

Timm, Ingo J.; Reuter, Lukas; Ulfert, Anna-Sophie; Ellwart, Thomas; Antoni, Conny H.

doi:10.24251/hicss.2019.659

Cited by 3 publications

(3 citation statements)

References 31 publications

(32 reference statements)

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“…Optimal solutions may be obtained by learning from human cooperative systems using psychologic research. Timm et al analyzed intentional forgetting as an interdisciplinary concept to adapt human and AI-teams in the German Priority Research Program on Intentional Forgetting in Organizations (SPP 1921, funded by the DFG) (Timm et al 2019). On this basis, an MAS extension for situational adaptation of processes and roles by learning and forgetting of actions, plans, and desires has been developed (Reuter et al 2020;Timm et al 2020).…”

Section: Advanced Concepts For Multiagent Systems In Logisticsmentioning

confidence: 99%

Intelligent Agents for Social and Learning Logistics Systems

Herzog

Timm

2021

Dynamics in Logistics

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The digitalization of logistics processes is often based on distributed models and decentralized control. As these logistics models constitute an important part of Industrie 4.0 concepts they must be powerful enough to cover dynamic processes and must enable a host of functions such as goal-oriented, reactive, pro-active, communicative, cooperative, competitive, and learning behaviors. In addition, these distributed models must allow for simulating, planning, allocating, scheduling, and optimizing logistics tasks. This implies that they must be able to act through communication channels with each other thus establishing logistics social communities.Multiagent Systems (MAS) have been around for more than 30 years and lend themselves to the implementation of these distributed models needed for autonomous and cooperating logistics processes. It will be described and also demonstrated by three case studies why MAS are well suited for social and learning logistics systems. It will be shown how the resulting distributed MAS models provide the required functionalities for production and transportation logistics including the handling of dynamic local events as an essential feature for the successful planning, scheduling, optimizing, monitoring, and control of global logistics processes.

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Section: Advanced Concepts For Multiagent Systems In Logisticsmentioning

confidence: 99%

Intelligent Agents for Social and Learning Logistics Systems

Herzog

Timm

2021

Dynamics in Logistics

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“…To assess preconditions to implement the approach in real clinical environments we will conduct an implementation study to evaluate acceptance [72][73][74] and trust [75,76] by physicians and patients and determine if the automated approach improves the coordination [77][78][79] between doctors more efficiently.…”

Section: Future Workmentioning

confidence: 99%

High-performance Diagnosis of Sleep Disorders: A Novel, Accurate and Fast Machine Learning Approach Using Electroencephalographic Data

Buettner¹,

Grimmeisen²,

Gotschlich³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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While diagnosing sleep disorders by physicians using electroencephalographic data is protracted and inaccurate, we report promising results from a novel, fast and reliable machine learning approach. Our approach only needs an electroencephalographic recording snippet of 10 minutes instead of eight hours to correctly classify the disorder with an accuracy of over 90 percent. The Rapid Eye Movement sleep behavior disorder can lead to secondary diseases like Parkinson or Dementia. Therefore, it is important to classify the disorder fast and with a high level of accuracy -which is now possible with our approach.

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“…• to evaluate technology acceptance [84,85,86,87] and trust [88] of our embodied module and confirm if the automated approach improves the coordination [89,90,91,92,93,94,95] more efficiently.…”

Section: Future Workmentioning

confidence: 99%

Development of a Highly Precise Place Recognition Module for Effective Human-robot Interactions in Changing Lighting and Viewpoint Conditions

Baumgartl¹,

Buettner²

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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We present a highly precise and robust module for indoor place recognition, extending the work by Lemaignan et al. and Robert Jr. by giving the robot the ability to recognize its environment context. We developed a full end-to-end convolutional neural network architecture, using a pre-trained deep convolutional neural network and the explicit inductive bias transfer learning strategy. Experimental results based on the York University and Rzeszów University dataset show excellent performance values (over 94.75 and 97.95 percent accuracy) and a high level of robustness over changes in camera viewpoint and lighting conditions, outperforming current benchmarks. Furthermore, our architecture is 82.46 percent smaller than the current benchmark, making our module suitable for embedding into mobile robots and easily adoptable to other datasets without the need for heavy adjustments.

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Analyzing the Effects of Role Configuration in Logistics Processes using Multiagent-Based Simulation: An Interdisciplinary Approach

Cited by 3 publications

References 31 publications

Intelligent Agents for Social and Learning Logistics Systems

Intelligent Agents for Social and Learning Logistics Systems

High-performance Diagnosis of Sleep Disorders: A Novel, Accurate and Fast Machine Learning Approach Using Electroencephalographic Data

Development of a Highly Precise Place Recognition Module for Effective Human-robot Interactions in Changing Lighting and Viewpoint Conditions

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