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ORCIDThomas Gabel
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2020 – today
- 2022
[c29]Thomas Gabel, Fabian Sommer:
Case-Based Learning and Reasoning Using Layered Boundary Multigraphs. ICCBR 2022: 193-208- [c28]Thomas Gabel, Fabian Sommer:
Instance-Based Opponent Action Prediction in Soccer Simulation Using Boundary Graphs. RoboCup 2022: 14-26 - 2020
- [j6]Alaa Tharwat, Thomas Gabel:
Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm. Neural Comput. Appl. 32(11): 6925-6938 (2020)
2010 – 2019
- 2019
- [j5]Alaa Tharwat, Mohamed Elhoseny
, Aboul Ella Hassanien, Thomas Gabel, Arun Kumar Sangaiah:
Intelligent Bézier curve-based path planning model using Chaotic Particle Swarm Optimization algorithm. Clust. Comput. 22(Supplement): 4745-4766 (2019) - [c27]Thomas Gabel, Eicke Godehardt:
On the Generalization Capabilities of Sharp Minima in Case-Based Reasoning. ICCBR 2019: 79-94 - 2018
- [j4]Alaa Tharwat, Essam H. Houssein, Mohammed M. Ahmed, Aboul Ella Hassanien, Thomas Gabel:
MOGOA algorithm for constrained and unconstrained multi-objective optimization problems. Appl. Intell. 48(8): 2268-2283 (2018) - [c26]Thomas Gabel, Philipp Klöppner, Eicke Godehardt, Alaa Tharwat:
Communication in Soccer Simulation: On the Use of Wiretapping Opponent Teams. RoboCup 2018: 3-15 - 2017
- [c25]Alaa Tharwat, Thomas Gabel, Aboul Ella Hassanien:
Classification of Toxicity Effects of Biotransformed Hepatic Drugs Using Optimized Support Vector Machine. AISI 2017: 161-170 - [c24]Alaa Tharwat, Thomas Gabel, Aboul Ella Hassanien:
Parameter Optimization of Support Vector Machine Using Dragonfly Algorithm. AISI 2017: 309-319 - [c23]Suleyman Gemici, Thomas Gabel, Benjamin Loffler, Alaa Tharwat:
Combining evolutionary algorithms and case-based reasoning for learning high-quality shooting strategies in AI birds. EAIS 2017: 1-8 - [c22]Thomas Gabel, Alaa Tharwat, Eicke Godehardt:
Eavesdropping Opponent Agent Communication Using Deep Learning. MATES 2017: 205-222 - 2016
- [c21]Thomas Gabel, Egbert Falkenberg, Eicke Godehardt:
Progress in RoboCup Revisited: The State of Soccer Simulation 2D. RoboCup 2016: 144-156 - 2015
- [c20]Thomas Gabel, Eicke Godehardt:
I Know What You're Doing: A Case Study on Case-Based Opponent Modeling and Low-Level Action Prediction. ICCBR (Workshops) 2015: 13-22 - [c19]Thomas Gabel, Eicke Godehardt:
Top-Down Induction of Similarity Measures Using Similarity Clouds. ICCBR 2015: 149-164 - 2012
- [p1]Sascha Lange, Thomas Gabel, Martin A. Riedmiller:
Batch Reinforcement Learning. Reinforcement Learning 2012: 45-73 - 2011
- [c18]Thomas Gabel, Christian Lutz, Martin A. Riedmiller:
Improved neural fitted Q iteration applied to a novel computer gaming and learning benchmark. ADPRL 2011: 279-286 - 2010
- [c17]Thomas Gabel, Martin A. Riedmiller:
On Progress in RoboCup: The Simulation League Showcase. RoboCup 2010: 36-47
2000 – 2009
- 2009
- [b1]Thomas Gabel:
Multi-agent reinforcement learning approaches for distributed job shop scheduling problems. University of Osnabrück, 2009, pp. 1-161 - [j3]Martin A. Riedmiller, Thomas Gabel, Roland Hafner, Sascha Lange:
Reinforcement learning for robot soccer. Auton. Robots 27(1): 55-73 (2009) - [i1]Thomas Gabel:
Cooperative Multi-Agent Systems from the Reinforcement Learning Perspective - Challenges, Algorithms, and an Application. Algorithmic Methods for Distributed Cooperative Systems 2009 - 2008
- [c16]Thomas Gabel, Martin A. Riedmiller:
Reinforcement learning for DEC-MDPs with changing action sets and partially ordered dependencies. AAMAS (3) 2008: 1333-1336 - [c15]Thomas Gabel, Martin A. Riedmiller:
Increasing Precision of Credible Case-Based Inference. ECCBR 2008: 225-239 - [c14]Thomas Gabel, Martin A. Riedmiller:
Evaluation of Batch-Mode Reinforcement Learning Methods for Solving DEC-MDPs with Changing Action Sets. EWRL 2008: 82-95 - [c13]Thomas Gabel, Martin A. Riedmiller:
Joint Equilibrium Policy Search for Multi-Agent Scheduling Problems. MATES 2008: 61-72 - [c12]Thomas Gabel, Martin A. Riedmiller, Florian Trost:
A Case Study on Improving Defense Behavior in Soccer Simulation 2D: The NeuroHassle Approach. RoboCup 2008: 61-72 - 2007
- [c11]Martin A. Riedmiller, Thomas Gabel:
On Experiences in a Complex and Competitive Gaming Domain: Reinforcement Learning Meets RoboCup. CIG 2007: 17-23 - [c10]Thomas Gabel, Martin A. Riedmiller:
Scaling Adaptive Agent-Based Reactive Job-Shop Scheduling to Large-Scale Problems. CISched 2007: 259-266 - [c9]Thomas Gabel, Martin A. Riedmiller:
An Analysis of Case-Based Value Function Approximation by Approximating State Transition Graphs. ICCBR 2007: 344-358 - 2006
- [j2]Martin A. Riedmiller, Thomas Gabel, Roland Hafner, Sascha Lange, Martin Lauer:
Die Brainstormers: Entwurfsprinzipien lernfähiger autonomer Roboter. Inform. Spektrum 29(3): 175-190 (2006) - [j1]Thomas Gabel, Martin A. Riedmiller:
Learning a Partial Behavior for a Competitive Robotic Soccer Agent. Künstliche Intell. 20(2): 18-23 (2006) - [c8]Armin Stahl, Thomas Gabel:
Optimizing Similarity Assessment in Case-Based Reasoning. AAAI 2006: 1667-1670 - [c7]Thomas Gabel, Martin A. Riedmiller:
Reducing policy degradation in neuro-dynamic programming. ESANN 2006: 653-658 - [c6]Thomas Gabel, Martin A. Riedmiller:
Multi-agent Case-Based Reasoning for Cooperative Reinforcement Learners. ECCBR 2006: 32-46 - 2005
- [c5]Thomas Gabel, Martin A. Riedmiller:
CBR for State Value Function Approximation in Reinforcement Learning. ICCBR 2005: 206-221 - [c4]Thomas Gabel:
On the Use of Vocabulary Knowledge for Learning Similarity Measures. Wissensmanagement 2005: 253-258 - [c3]Thomas Gabel:
On the Use of Vocabulary Knowledge for Learning Similarity Measures. Wissensmanagement (LNCS Volume) 2005: 272-283 - 2004
- [c2]Thomas Gabel, Armin Stahl:
Exploiting Background Knowledge when Learning Similarity Measures. ECCBR 2004: 169-183 - 2003
- [c1]Armin Stahl, Thomas Gabel:
Using Evolution Programs to Learn Local Similarity Measures. ICCBR 2003: 537-551
Coauthor Index
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