Julian Groß's research while affiliated with Deutsches Forschungszentrum für Künstliche Intelligenz and other places
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Publications (4)
The current trend of using artificial neural networks to solve computationally intensive problems is omnipresent. In this scope, DeepQ learning is a common choice for agent-based problems. DeepQ combines the concept of Q-Learning with (deep) neural networks to learn different Q-values/matrices based on environmental conditions. Unfortunately, DeepQ...
For years, the manufacturing industry has been investing substantial amounts of research and development work for the implementation of hybrid teams of human workers and robotic units. The composition of hybrid teams requires an optimal coordination of individual players with fundamentally different characteristics and skills. In this paper, we pre...
Graphics Processing Units (GPUs) are widely spread nowadays due to their parallel processing capabilities. Leveraging these hardware features is particularly important for computationally expensive tasks and workloads. Prominent use cases are optimization problems and simulations that can be parallelized and tuned for these architectures. In the ge...
Many optimization problems (especially nonsmooth ones) are typically solved by genetic, evolutionary, or metaheuristic-based algorithms. However, these genetic approaches and other related papers typically assume the existence of a neighborhood or successor-state function N(x), where x is a candidate state. The implementation of such a function can...
Citations
... We also propose a novel GPU-assisted approach for on-the-fly generation of training samples for Deep-Q learning [4], a powerful solution for the main challenge of providing training data for such techniques, which outperforms conventional approaches both in terms of run-time and memory/storage consumption. It uses the domain description extracted from a process model in order to iteratively instantiate randomized states and compute corresponding Q-matrices. ...
... Particularly approaches based on commercial simulation tools are designed to iteratively draft and test candidate HRC workflows by precisely entering work items for all involved agents. In contrast, Antakli et al. [203] have proposed a simulation architecture for more interactive testing with coupled agent behaviours: Production planners can here create different situations by manipulating objects and agent states at simulation runtime, hence influencing the course of actions emerging from a nearoptimal optimization scheme and human motion synthesis. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/644570245259264-1530688930116_Q64/Andre-Antakli.jpg)
![[object Object]](https://i1.rgstatic.net/ii/profile.image/1006618036682752-1617007850133_Q64/Torsten-Spieldenner.jpg)
... In the field of compiler construction, a state contains all variable → value bindings in the current context. However, in other domains (e.g. in the field of heuristic search or optimization), it is not sufficient to investigate a single state at a time [10,11]. These states can be tracked in parallel and the interpreter has to be adjusted in a way that it can be applied to several states simultaneously. ...
