David Rother, Thomas Weisswange, Jan Peters,
"Summary: Disentangling Interaction using Maximum Entropy Reinforcement Learning in Multi-Agent Systems",
AAAI 2023 Fall Symposia: Agent Teaming in Mixed-Motive Situations, 2023.
Abstract
Research on multi-agent interaction involving both artifi-
cial agents and humans is still in its infancy. Current ap-
proaches often focus on collaboration-centered human be-
havior or a limited set of predefined situations, potentially
limiting their efficacy in ”coexistence” environments. These
are scenarios likely to arise in future deployments of robots
in human-inhabited spaces, where interactions won’t always
align with predef...
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Theodoros Stouraitis and Michael Gienger,
"Predictive and Robust Robot Assistance for Sequential Manipulation Tasks",
IEEE Research and Automation Letters (RA-L), 2023.
Abstract
This paper presents a novel concept to support impaired users in daily physical object manipulation tasks with a robot. Starting out with an assumed manipulation task of a user, we propose a predictive model that uniquely casts the user's sequential behavior as well as a robot support intervention into a hierarchical multi-objective optimization problem. A major contribution is the prediction formulation, which allows to model several different f...
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Thomas Jatschka, Matthias Rauscher, Tobias Rodemann, Guenther Raidl,
"A Large Neighborhood Search for Battery Swapping Station Location Planning for Electric Scooters",
EuroCAST Conference 2022, pp. 121-129, 2023.
Abstract
We consider the Multi Objective Battery Swapping Station Location Problem (MOBSSLP) for planning the setup of new stations for exchanging depleted batteries of electric scooters with the aim of minimizing a three-part objective function while satisfying an expected amount of demand. Batteries returned at a station are charged and provided to customers again once they are full. We present a large neighborhood search (LNS) for solving MOBSSLP insta...
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Steffen Limmer and Nils Einecke,
"SPOC 2023: Approaches by HRI",
Space Optimization Competition Workshop, 2023.
Abstract
Present approach for 2023 ESA / GECCO optimization challenge at special SpOC workshop held by the ESA....
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Jiawen Kong,
"Learning Class-Imbalanced Problems from the Perspective of Data Intrinsic Characteristics",
Leiden University, 2023.
Abstract
The class-imbalance problem is a challenging classification task and is frequently encountered in real-world applications. Various techniques have been developed to improve the imbalanced classification performance theoretically and practically. Apart from developing new approaches, researchers also address the importance of understanding the data itself, which will provide more insight into what actually hinders the imbalanced classification per...
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Sibghat Ullah,
"Model-assisted robust optimization for continuous black-box problems",
Leiden University, 2023.
Abstract
While solving real-world optimization problems, e.g., in the area of automotive engineering, building construction, and steel production, the issue of uncertainty and noise is frequently-encountered. Common sources of uncertainty and noise include search/decision variables (that describe the system to be optimized), the environmental variables or operating conditions the system is subject to, the evaluation of the (physical) system (or model of t...
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Margarita Veshchezerova, Mikhail Somov, David Bertsche, Steffen Limmer, Sebastian Schmitt, Michael Perelshtein, Ayush Joshi,
"A Hybrid Quantum-Classical Approach to the Electric Mobility Problem",
IEEE Quantum Week 2023, 2023.
Abstract
We suggest a hybrid quantum-classical routine for the NP-hard Electric Vehicle Fleet Charging and Allocation Problem. The original formulation is a Mixed Integer Linear Program with continuous variables and inequality constraints. To separate inequality constraints that are difficult for quantum routines we use a decomposition in master and pricing problems: the former targets the assignment of vehicles to reservations and the latter suggests veh...
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Felix Lanfermann,
"Concept Identification for Complex Data Sets",
Bielefeld University, 2023.
Abstract
Large and complex data sets play an essential role in many engineering and computer science applications. Revealing structures within data sets, such as groups of similar data samples or correlations between feature values, is often desirable. But generating such insights is far from trivial. The field of concept identification targets to automatically find groups of data samples in large and complex data sets which share common properties. Such ...
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Frank Joublin, Antonello Ceravola, Pavel Smirnov, Felix Ocker, Jörg Deigmöller, Anna Belardinelli, Chao Wang, Daniel Tanneberg, Stephan Hasler, Michael Gienger,
"CoPal: Planning Robot Actions using Large Language Models",
Arxiv, no. arXiv:2310.07263, 2023.
Abstract
Recent advances in the field of pretrained Large Language Models (LLM) made commonsense knowledge available "out of the box" for a vast range of scenarios including content generation, customer service, and voice assistants. The release of GPT-3.5 (known as ChatGPT) opened prospectives for building highly contextualizable conversational agents, capable to hold a dialog and reflect about various situations as well as on behalf of different social ...
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Thiago Rios, Stefan Menzel, Bernhard Sendhoff,
"Large Language and Text-to-3D Models for Engineering Design Optimization",
IEEE Symposium Series on Computational Intelligence, 2023.
Abstract
The current advances in generative AI for learning large neural network models with the capability to produce essays, images, music and even 3D assets from text prompts create opportunities for a manifold of disciplines. In the present paper, we study the potential of deep text-to-3D models in the engineering domain, with focus on the chances and challenges when integrating and interacting with 3D assets in computational simulation-based design o...
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