Artificial Intelligence and Machine Learning: 33rd Benelux Conference on Artificial Intelligence, BNAIC/Benelearn 2021, Esch-sur-Alzette, Luxembourg, November ... Computer and Information Science Book 1530)

Preface
Organization
Contents
Annotating Data
Active Learning for Reducing Labeling Effort in Text Classification Tasks
	1 Introduction
	2 Related Work
	3 Methods
		3.1 Active Learning
		3.2 Model Architecture
		3.3 Query Functions
		3.4 Heuristics
		3.5 Experimental Setup
	4 Results
		4.1 Active Learning
		4.2 Query-Pool Size
		4.3 Heuristics
	5 Discussion
		A.1  RET Algorithm Computational Cost Analysis
		A.2  Algorithms
	References
Refining Weakly-Supervised Free Space Estimation Through Data Augmentation and Recursive Training
	1 Introduction
	2 Related Work
		2.1 Supervised Learning for Segmentation
		2.2 Weakly-Supervised Semantic Segmentation
		2.3 Unsupervised and Weakly-Supervised Monocular Free Space Segmentation
		2.4 Training Strategies for Weakly-Supervised Segmentation
	3 Methodology
		3.1 Data Augmentation
		3.2 Recursive Training
	4 Experimental Setup
		4.1 Dataset
		4.2 Evaluation Metrics
		4.3 Network Architectures
		4.4 Training Procedure
		4.5 Use of Ground Truth Data
	5 Results
		5.1 Fully-Supervised Results
		5.2 Unsupervised and Weakly-Supervised Baselines
		5.3 Data Augmentation and Recursive Training
		5.4 Limits of Recursive Training
		5.5 Qualitative Results
	6 Conclusion
	References
Self-labeling of Fully Mediating Representations by Graph Alignment
	1 Introduction
	2 Related Work
	3 Self-labeling of Fully Mediating Representations
		3.1 Graph Alignment
		3.2 Method
	4 Experiments
	5 Conclusion
	A  Appendix
		A.1  Architecture Summary of Graph Recognition Tool
		A.2  Training Details for Graph Recognition Tool
		A.3  Computational Cost per Rich-Labeling Iteration
		A.4  Examples of Cases Where Graph Alignment Fails
	References
Recognizing Objects
Task Independent Capsule-Based Agents for Deep Q-Learning
	1 Introduction
	2 Background
		2.1 Capsule Networks
		2.2 Deep Reinforcement Learning
	3 Related Work
	4 Methodology
	5 Analysis
		5.1 Cumulative Reward and Parameters
		5.2 Input State
		5.3 Action Space
		5.4 Reward
	6 Discussion
		6.1 Training
		6.2 Environment
	7 Conclusion
	References
Object Detection with Semi-supervised Adversarial Domain Adaptation for Real-Time Edge Devices
	1 Introduction
	2 Related Work
		2.1 Object Detection
		2.2 Domain Adaptation
	3 Proposed Method
		3.1 Adversarial Domain Adaptation for Object Detection
	4 Implementation Details
	5 Evaluation
		5.1 Datasets
		5.2 Experiments
	6 Conclusion
	References
Explaining Outcomes
Exploring Explainable AI in the Financial Sector: Perspectives of Banks and Supervisory Authorities
	Abstract
	1 Introduction
	2 Theoretical Background
	3 Research Method
		3.1 Use Cases
		3.2 Data Collection
		3.3 Data Analysis
	4 Results
		4.1 Consumer Credit
		4.2 Credit Risk Management
		4.3 Anti-money Laundering (AML)
		4.4 General
	5 Discussion and Conclusions
	References
The Effect of Noise Level on the Accuracy of Causal Discovery Methods with Additive Noise Models
	1 Introduction
	2 Related Work
	3 Causal Discovery Methods
		3.1 Notations
		3.2 Regression with Subsequent Independence Test (Resit)
		3.3 Identification Using Conditional Variances (Uncertainty Scoring)
	4 Experimental Setup
	5 Experimental Results
		5.1 Resit
		5.2 Uncertainty Scoring
	6 Conclusions
	References
A Bayesian Framework for Evaluating Evolutionary Art
	1 Introduction
	2 Background
		2.1 Evaluating Computer-Generated Art
	3 The Bayesian Framework
		3.1 Art Turing Test
		3.2 Bayesian Model Comparison
	4 Application
		4.1 Tree Representation
		4.2 The Mathematical Fitness Function
		4.3 Results and Analysis
	5 Questionnaire
	6 Code Base
	7 Discussion
	8 Conclusion
	References
Understanding Language
Dutch SQuAD and Ensemble Learning for Question Answering from Labour Agreements
	1 Introduction
	2 Related Work
	3 Datasets
		3.1 Dutch SQuAD v2.0
		3.2 Labour Agreement Dataset
	4 Approach
		4.1 Fine-Tuning
		4.2 Voted BERT
	5 Evaluation
		5.1 Models
		5.2 Evaluation Metrics
	6 Results
		6.1 Dutch SQuAD
		6.2 Labour Agreement Dataset
	7 Discussion
		7.1 Conclusion
	References
Verbalizing but Not Just Verbatim Translations of Ontology Axioms
	1 Introduction
	2 Related Work
	3 Preliminaries and Defintions
	4 Proposed Verbalization Approach
	5 Semantic-Refinement of Label-Sets
	6 Empirical Evaluation
		6.1 Results and Discussions
	7 Conclusion
	References
Transfer Learning and Curriculum Learning in Sokoban
	1 Introduction
	2 Related Work
	3 Experimental Setup
		3.1 Neural Network Architecture
		3.2 Transfer Approach
	4 Experiments
		4.1 Transfer Among Related Tasks
		4.2 Transfer Among Different Tasks (SL/RL)
		4.3 Transfer to Different Appearance
		4.4 Visualizing Agent Detection
	5 Conclusion and Future Work
	References
Reinforcing Decisions
Proximal Policy Optimisation for a Private Equity Recommitment System
	1 Introduction
	2 Related Works
	3 Problem Description
	4 Proximal Policy Optimisation
	5 Private Equity Recommitment as RL Problem
		5.1 Modelling
		5.2 Synthetic Cashflows
	6 Experimental Setups
	7 Experimental Results
	8 Conclusion
	References
Regular Decision Processes for Grid Worlds
	1 Introduction
	2 Background
		2.1 Markov Decision Processes
		2.2 Non-Markovian Decision Processes
		2.3 Temporal Logic, Automata and Product MDPs
		2.4 Regular Decision Processes: Non-Markovian Dynamics
		2.5 Related Work
	3 Approach and Software Design
		3.1 Compilation: From RDP to MDP
	4 Experiments
		4.1 Experiment 1: Goal Sparsity
		4.2 Experiment 2: Reward Shaping
		4.3 Experiment 3: Safety
		4.4 Experiment 4: Non-Markovian Transitions
	5 Conclusions and Future Work
	References
MoveRL: To a Safer Robotic Reinforcement Learning Environment
	1 Introduction
	2 Notations
	3 Related Work
		3.1 RL Robotic Environment
		3.2 Safe Reinforcement Learning
		3.3 Path Planning
	4 Contribution
		4.1 The Gym Environment
		4.2 Observation Space
		4.3 Action Space
		4.4 Why Do We Need Sequences of Actions?
		4.5 Reward Function
		4.6 Initial States and Termination
		4.7 Safety Guarantee
	5 Experiment
		5.1 Learning Scenarios
		5.2 Learning Algorithm
		5.3 Results
	6 Conclusion
	References
Author Index