document

The generalizability of argument quality dimensions in NLP models

Nguyen, Jakub (author)

This research revolves around measuring the quality of arguments. High-quality arguments help in improving political discussions, resulting in better decision-making. Wachsmuth et al. developed a taxonomy breaking down argument quality into several dimensions. This work makes use of that taxonomy and combines it with modern NLP models. A cross...

master thesis 2023

document

Constructing a Pluralist Moral Sentence Embedding Space using Contrastive Learning

Park, Jeongwoo (author)

Moral values influence humans in decision-making. Pluralist moral philosophers argue that human morality can be represented by a finite number of moral values, respecting the differences in moral views. Recent advancements in NLP show that language models retain a discernible level of knowledge in deontological ethics and moral norms of society....

master thesis 2023

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NLP and reinforcement learning to generate morally aligned text: How does explainable models perform compared to black-box models

De Leeuw, Nathaniël (author)

<br/>This paper evaluates the performance of an automated explainable model, Moral- Strength, to predict morality, or more pre- cisely Moral Foundations Theory (MFT) traits. MFT is a way to represent and divide morality into precise and detailed traits. This evaluation happens in the Jiminy Cricket environment, an environ- ment composed of 25...

bachelor thesis 2023

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Natural Language Processing and Reinforcement Learning to Generate Morally Aligned Text: Comparing a moral agent to an optimally playing agent

Lubbers, Rob (author)

Nowadays Large Language Models are becoming more and more prevalent in today's society. These models act without a sense of morality however. They only prioritize accomplishing their goal. Currently, little research has been done evaluating these models. The current state of the art Reinforcement Learning models represent morality by a singular...

bachelor thesis 2023

document

Natural Language Processing and Reinforcement Learning to Generate Morally: What is the optimal weight w to win the games while playing morally?

Boudier, Kenzo (author)

In our everyday life, people interact more and more with agents. However these agents often lack a moral sense and prioritize the accomplishment of the given task. In consequence, agents may unknowingly act immorally. Little research or progress has been done to endow agents with human morality and an internal sense of right and wrong. As of...

bachelor thesis 2023

document

Balancing multidimensional morality and progression: Evaluating the tradeoff for artificial agents playing text-based games

Şerbănescu, Bianca (author)

Morality is a fundamental concept that guides humans in the decision-making process. Given the rise of large language models in society, it is necessary to ensure that they adhere to human principles, among which morality is of substantial importance. While research has been done regarding artificial agents behaving morally, current state of the...

bachelor thesis 2023

document

Active Learning with Multi-annotator Disagreement

Schrama, Mei Lan (author)

Moral values are often used as guidelines for human behaviour. The ability to identify moral values is important for social and ethical artificial intelligence. We address the difficulties of using contemporary natural language processing (NLP) techniques to classify moral values in texts. As the classification criterion of moral values is...

master thesis 2022

document

Training a Negotiating Agent through Self-Play

Jurševskis, Renāts (author)

Recent developments in applying reinforcement learning to cooperative environments, like negotiation, have brought forward an important question: how well can a negotiating agent be trained through self-play? Previous research has seen successful application of self-play to other settings, like the games of chess and Go. This paper explores the...

bachelor thesis 2022

document

Learning the Problem Representation for Improving Negotiation Strategies

Fledderus, Eddy (author)

The domains of the negotiation can vary significantly. It is possible that a domain is very cooperative, where both agents can receive a high utility; the opposite is also possible, where the domain is very competitive and the agents cannot both get a high utility. In the same manner, the agents can have different strategies leading to a...

bachelor thesis 2022

document

Opponent Modeling in Automated Bilateral Negotiation: Can Machine Learning Techniques Outperform State-of-the-Art Heuristic Techniques?

Pocola, Octav (author)

Automated negotiation agents can highly benefit from learning their opponent’s preferences. Multiple algorithms have been developed with the two main categories being: heuristic techniques and machine learning techniques. Historically, heuristic techniques have dominated the field, but with the recent development in the field of machine learning...

bachelor thesis 2022

document

Learning a Latent Representation of the Opponent in Automated Negotiation

Gaghi, Radu (author)

This paper introduces a strategy for learning opponent parameters in automated negotiation and using them for future negotiation sessions. The goal is to maximize the agent’s utility while being consistent in its performance over various negotiation scenarios. While a number of reinforcement learning approaches in the field have used Q-learning,...

bachelor thesis 2022

document

Improving a Reinforcement Learning Negotiating Agent’s Performance by Extracting Information from the Opponent’s Sequence of Offers

Agrawal, Arpit (author)

With the prospects of decentralized multi-agent systems becoming more prevalent in daily life, automated negotiation agents have made their place in these collaborative settings. They are an approach to promote communication between the agents in reaching solutions that are better for all involved.<br/><br/>Recent literature has shown great...

bachelor thesis 2022

document

Unravelling Twitter chaos during a policy crisis: Applying Sentiment Analysis and Topic Modelling to Tweets about the Dutch Nitrogen Crisis

Hendrikse, Mila (author)

In May 2019, the Dutch Council of State rejected the national approach for reducing nitrogen emissions in Dutch nature. Farmers were targeted by the policy change: all licenses for agricultural expansion were revoked, affecting the financial livelihoods of farmers. Farmers did not take this well and, using social media platforms, started...

master thesis 2021

document

Moral Embeddings: A closer look at their Performance, Generalizability and Transferability

Vecerdea, Dragos (author)

Moral values are abstract ideas that ground our judgements towards what is right or wrong. However, with the rapid unfold of moral rhetoric on social media, it becomes increasingly important to place these ideas in a moral frame, contain their harmful effects, and recognise their positive ones. So far, estimating values from opinionated text has...

bachelor thesis 2021

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Estimating the transferability of state-of-the-art models in predicting moral values

Dondera, Alin (author)

Moral values play a crucial role in our decision-making process by defining what is right and wrong. With the emergence of political activism and moral discourse on social media, and the latest developments in Natural Language Processing, we are looking at an opportunity to analyze moral values to observe trends as they form. Recent studies have...

bachelor thesis 2021

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Evaluating catastrophic forgetting of state-of-the-art NLP models for predicting moral values

Arsene, Florentin (author)

Personal moral values represent the motivation behind individuals' actions and opinions. Understanding these values is helpful both in predicting individuals' actions, such as violent protests, and building AI that can better collaborate with humans. Predicting moral values is a challenging problem due to the abstract and subjective essence of...

bachelor thesis 2021

document

Evaluating interpretability of state-of-the-art NLP models for predicting moral values

Constantinescu, Ionut (author)

Understanding personal values is a crucial aspect that can facilitate the collaboration between AI and humans. Nonetheless, the implementation of collaborative agents in real life greatly depends on the amount of trust that is built in their relationship with people. In order to bridge this gap, more extensive analysis of the explainability of...

bachelor thesis 2021

document

Performance analysis of the state-of-the-art NLP models for predicting moral values

Geadău, Andrei (author)

Moral values are instrumental in understanding people's beliefs and behaviors. Estimating such values from text would facilitate the interaction between humans and computers. To date, no comparison between NLP models for predicting moral values from text exists. This paper addresses this by comparing LSTM and more novel models such as BERT and...

bachelor thesis 2021

document

Deep Learning for Stance Detection: A Review and Comparison of the State-of-the-Art Approaches

Roeters van Lennep, Jacob (author)

Stance detection is a Natural Language Processing task that can detect if the input text is in favour, against or neutral towards a target. Research on stance detection has been growing and evolving over the last decade. In this paper, the current approaches for stance detection are discussed with a focus on the deep learning approaches. The...

bachelor thesis 2021

document

Political Stance Detection using Knowledge Graphs and Sentiment Analysis

Van Steenweghen, Abel (author)

Sentiment analysis techniques estimate the opinion of the au- thor of a text towards an entity from that text. Current sen- timent analysis techniques are based on language features or deep learning methods. However, they do not make use of the extensive background knowledge that human readers can have. This makes it difficult for these models...

bachelor thesis 2021