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Qiqi Liu, Ran Cheng, Yaochu Jin, Martin Heiderich, Tobias Rodemann, "Reference Vector Assisted Adaptive Model Management for Surrogate-Assisted Many-objective Optimization", IEEE Transactions on Systems, Man and Cybernetics: Systems, 2022.

Abstract

Acquisition functions for surrogate-assisted many-objective optimization require a delicate balance between convergence and diversity. To meet this requirement, we propose an adaptive model management strategy assisted by two sets of reference vectors, one set of adaptive reference vectors accounting for convergence while the other set of fixed reference vectors for diversity. Specifically, we first propose a new acquisition function that calcu...



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Muhammad Haris, Mathias Franzius, Ute Bauer-Wersing, "Physical Interactive Localization Learning ", 2022 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO), 2022.

Abstract

Localization is fundamental for mobile robots, especially in unconstrained outdoor environments. Earlier work showed unsupervised localization learning on landmarks to be suitable for large-scale scenes. However, this relied on hand-labeled data to train a CNN for recognizing landmarks. We propose a new approach that allows a robot to learn landmarks for localization with a human cooperatively. This approach uses pre-trained detectors of c...



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Nivesh Dommaraju, Mariusz Bujny, Stefan Menzel, Markus Olhofer, Fabian Duddeck, "Evaluation of geometric similarity metrics for structural clusters generated using topology optimization", Applied Intelligence, 2022.

Abstract

In an engineering design process, multitudes of feasible designs can be automatically generated using structural optimization methods by varying the design requirements or user preferences for different performance objectives. Design exploration of such potentially large datasets is a challenging task. An unsupervised data-centric approach for exploring designs is to find clusters of similar designs and recommend only the cluster representat...



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Thomas Schnürer, Malte Probst , Horst-Michael Groß, "Utilizing Emergent, Task-Independent Knowledge Representations for Accelerated Task-Learning in Reinforcement Learning", Fifth International Workshop on Intrinsically-Motivated Open-ended Learning, Max Planck Institute for Intelligent Systems,, no. 5, 2022.

Abstract

An intelligent agent in a complex environment will face a great number of diverse tasks. Rather than learning task-specific representations, we aim to reuse learned aspects to drive the acquisition of new tasks by leveraging previously learned abstract knowledge. Building on recent work that has introduced an inductive bias for explicit knowledge separation, we explore the benefits of such separation for learning new tasks. With an environment...



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Simon Kohaut, "Hybrid Probabilistic Logic Programming for Mission Design in Multimodal Mobility", Technical University of Darmstadt, 2022.

Abstract

Reasoning on subjective observations of the environment to navigate through complex and dynamic scenarios is a fundamental concept to human behavior. Hence, over the course of history, a vast landscape of approaches to formalize and automize inference has emerged. From propositional to higher-order logic and from simple stochastic measures to robust statistics, the power of both symbolic and numeric methods for reasoning in discrete and continuou...



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Jens Engel, Thomas Schmitt, Tobias Rodemann, Jürgen Adamy, "Hierarchical Economic Model Predictive Control Approach for a Building Energy Management System With Scenario-Driven EV Charging", IEEE Transactions on Smart Grid, vol. 13, no. 4, pp. 3082 - 3093, 2022.

Abstract

To deal with the increasing number of EVs and their effect on the infrastructure, intelligent and coordinated charge management is necessary. In this paper, this problem is considered in the context of a commercial building energy management system (BEMS) with V2G-capable employee EV charging stations (EVCS). We propose a hierarchical economic model predictive control (EMPC) scheme for the operation of the BEMS and the integration of EV charge ma...



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Nils Einecke and Thomas Weisswange, "Detecting Availability to Facilitate Social Communication", ICRA Workshop: Exploring the Roles of Robots for Embodied Mediation, 2022.

Abstract

Modern communication has shifted strongly towards the digital domain. Technologies like chat applications and messengers provide means to connect with anyone at anytime. However, being always reachable can create stress and provides potential for social pressure due to expected timely responses. In contrast, the classical communication types via telephone is based on dedicated time slots for an ongoing communication, but suffers from the need to ...



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Qiqi Liu, Yaochu Jin, Martin Heiderich, Tobias Rodemann, "Surrogate-Assisted Evolutionary Optimization of Expensive Many-objective Irregular Problems", Knowledge-Based Systems, vol. 240, pp. 108197, 2022.

Abstract

Surrogate-assisted evolutionary algorithms are one effective approach to handling expensive problems and have attracted increasing attention over the past decades. However, existing surrogate-assisted evolutionary algorithms pay little attention to expensive many-objective problems with irregular Pareto fronts, also called irregular problems. In this study, we propose a surrogate-assisted evolutionary algorithm for dealing with expensive irregu...



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Steffen Limmer and Nils Einecke, "An Efficient Approach for Peak-load-aware Scheduling of Energy-intensive Tasks in the Context of a Public IEEE Challenge", Energies, vol. 15, no. 10, 2022.

Abstract

The shift towards renewable energy and decreasing battery prices have led to numerous installations of PV and battery systems in industrial and public buildings. Furthermore, the fluctuation of energy costs is increasing since energy sources based on solar and wind power depend on the weather situation. In order to reduce energy costs, it is necessary to plan energy-hungry activities while taking into account private PV production, battery capaci...



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Kyle Poland, Anja Sturm, Aaron Gutknecht, Patricia Wollstadt, Michael Wibral, Abdullah Makkeh, "On a differentiable partial information decomposition for continuous random variables and applications in (artificial) neural networks", Bernstein Conference 2021, 2021.

Abstract

Understanding information mechanisms inside complex systems often poses intricate questions. In neural systems, information is often represented by an ensemble of agents. Knowledge about how information is distributed amongst those agents can lead to insights about how to distribute relevant information about a problem over available agents. These agents can, for instance, be neurons that are recorded during stimulation, one may imagine spike tra...



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