15 citations · 32 across the 11 of their papers we have counts for
12 papers
Learning to Participate through Trading of Reward Shares
Michael Kölle, Tim Matheis, Philipp Altmann +1
Enabling autonomous agents to act cooperatively is an important step to integrate artificial intelligence in our daily lives. While some methods seek to stimulate cooperation by le…
Stochastic Market Games
Kyrill Schmid, Lenz Belzner, Robert Müller +2
Some of the most relevant future applications of multi-agent systems like autonomous driving or factories as a service display mixed-motive scenarios, where agents might have confl…
Decentralized scheduling through an adaptive, trading-based multi-agent system
Michael Kölle, Lennart Rietdorf, Kyrill Schmid
In multi-agent reinforcement learning systems, the actions of one agent can have a negative impact on the rewards of other agents. One way to combat this problem is to let agents t…
Towards Multi-Agent Reinforcement Learning using Quantum Boltzmann Machines
Tobias Müller, Christoph Roch, Kyrill Schmid +1
Reinforcement learning has driven impressive advances in machine learning. Simultaneously, quantum-enhanced machine learning algorithms using quantum annealing underlie heavy devel…
Solving Large Steiner Tree Problems in Graphs for Cost-Efficient Fiber-To-The-Home Network Expansion
Tobias Müller, Kyrill Schmid, Daniëlle Schuman +3
The expansion of Fiber-To-The-Home (FTTH) networks creates high costs due to expensive excavation procedures. Optimizing the planning process and minimizing the cost of the earth e…
Analysis of Feature Representations for Anomalous Sound Detection
Robert Müller, Steffen Illium, Fabian Ritz +1
In this work, we thoroughly evaluate the efficacy of pretrained neural networks as feature extractors for anomalous sound detection. In doing so, we leverage the knowledge that is…
Difficulty Classification of Mountainbike Downhill Trails utilizing Deep Neural Networks
Stefan Langer, Robert Müller, Kyrill Schmid +1
The difficulty of mountainbike downhill trails is a subjective perception. However, sports-associations and mountainbike park operators attempt to group trails into different level…
Memory Bounded Open-Loop Planning in Large POMDPs using Thompson Sampling
Thomy Phan, Lenz Belzner, Marie Kiermeier +3
State-of-the-art approaches to partially observable planning like POMCP are based on stochastic tree search. While these approaches are computationally efficient, they may still co…
Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies
Thomy Phan, Kyrill Schmid, Lenz Belzner +3
Decision making in multi-agent systems (MAS) is a great challenge due to enormous state and joint action spaces as well as uncertainty, making centralized control generally infeasi…
Preparing for the Unexpected: Diversity Improves Planning Resilience in Evolutionary Algorithms
Thomas Gabor, Lenz Belzner, Thomy Phan +1
As automatic optimization techniques find their way into industrial applications, the behavior of many complex systems is determined by some form of planner picking the right actio…
The Sharer's Dilemma in Collective Adaptive Systems of Self-Interested Agents
Lenz Belzner, Kyrill Schmid, Thomy Phan +2
In collective adaptive systems (CAS), adaptation can be implemented by optimization wrt. utility. Agents in a CAS may be self-interested, while their utilities may depend on other…
Leveraging Statistical Multi-Agent Online Planning with Emergent Value Function Approximation
Thomy Phan, Lenz Belzner, Thomas Gabor +1
Making decisions is a great challenge in distributed autonomous environments due to enormous state spaces and uncertainty. Many online planning algorithms rely on statistical sampl…