16 citations · 77 across the 21 of their papers we have counts for
21 papers
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…
Synthesizing Safe Policies under Probabilistic Constraints with Reinforcement Learning and Bayesian Model Checking
Lenz Belzner, Martin Wirsing
We propose to leverage epistemic uncertainty about constraint satisfaction of a reinforcement learner in safety critical domains. We introduce a framework for specification of requ…
The Holy Grail of Quantum Artificial Intelligence: Major Challenges in Accelerating the Machine Learning Pipeline
Thomas Gabor, Leo Sünkel, Fabian Ritz +5
We discuss the synergetic connection between quantum computing and artificial intelligence. After surveying current approaches to quantum artificial intelligence and relating them…
Trajectory annotation using sequences of spatial perception
Sebastian Feld, Steffen Illium, Andreas Sedlmeier +1
In the near future, more and more machines will perform tasks in the vicinity of human spaces or support them directly in their spatially bound activities. In order to simplify the…
Bayesian Surprise in Indoor Environments
Sebastian Feld, Andreas Sedlmeier, Markus Friedrich +2
This paper proposes a novel method to identify unexpected structures in 2D floor plans using the concept of Bayesian Surprise. Taking into account that a person's expectation is an…
Uncertainty-Based Out-of-Distribution Classification in Deep Reinforcement Learning
Andreas Sedlmeier, Thomas Gabor, Thomy Phan +2
Robustness to out-of-distribution (OOD) data is an important goal in building reliable machine learning systems. Especially in autonomous systems, wrong predictions for OOD inputs…
Emergent Escape-based Flocking Behavior using Multi-Agent Reinforcement Learning
Carsten Hahn, Thomy Phan, Thomas Gabor +2
In nature, flocking or swarm behavior is observed in many species as it has beneficial properties like reducing the probability of being caught by a predator. In this paper, we pro…
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…
Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning
Andreas Sedlmeier, Thomas Gabor, Thomy Phan +2
We consider the problem of detecting out-of-distribution (OOD) samples in deep reinforcement learning. In a value based reinforcement learning setting, we propose to use uncertaint…
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…
Inheritance-Based Diversity Measures for Explicit Convergence Control in Evolutionary Algorithms
Thomas Gabor, Lenz Belzner, Claudia Linnhoff-Popien
Diversity is an important factor in evolutionary algorithms to prevent premature convergence towards a single local optimum. In order to maintain diversity throughout the process o…
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…
Genealogical Distance as a Diversity Estimate in Evolutionary Algorithms
Thomas Gabor, Lenz Belzner
The evolutionary edit distance between two individuals in a population, i.e., the amount of applications of any genetic operator it would take the evolutionary process to generate…
Qualitative Assessment of Recurrent Human Motion
Andre Ebert, Michael Till Beck, Andy Mattausch +2
Smartphone applications designed to track human motion in combination with wearable sensors, e.g., during physical exercising, raised huge attention recently. Commonly, they provid…
QoS-Aware Multi-Armed Bandits
Lenz Belzner, Thomas Gabor
Motivated by runtime verification of QoS requirements in self-adaptive and self-organizing systems that are able to reconfigure their structure and behavior in response to runtime…
Stacked Thompson Bandits
Lenz Belzner, Thomas Gabor
We introduce Stacked Thompson Bandits (STB) for efficiently generating plans that are likely to satisfy a given bounded temporal logic requirement. STB uses a simulation for evalua…
Bayesian Verification under Model Uncertainty
Lenz Belzner, Thomas Gabor
Machine learning enables systems to build and update domain models based on runtime observations. In this paper, we study statistical model checking and runtime verification for sy…
Monte Carlo Action Programming
Lenz Belzner
This paper proposes Monte Carlo Action Programming, a programming language framework for autonomous systems that act in large probabilistic state spaces with high branching factors…
Scalable Multiagent Coordination with Distributed Online Open Loop Planning
Lenz Belzner, Thomas Gabor
We propose distributed online open loop planning (DOOLP), a general framework for online multiagent coordination and decision making under uncertainty. DOOLP is based on online heu…