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Non-stationary preference learning in online human-robot interaction
Traykov, A. (2025) Non-stationary preference learning in online human-robot interaction.
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| Abstract: | Dueling bandit algorithms are used for decision-making problems where feedback is qualitative or otherwise unavailable in a precise, discrete numerical format. A key limitation of standard dueling bandit algorithms is the assumption of a stationary environment, where in practice, many applications are influenced by changing preferences. Non-Stationary Dueling Bandits are a variation of the framework that introduces a dynamic layer to the decision process of the agent. Non-stationary dueling bandits have been researched less than other bandit problems, with most research being done within the last five years. There is an identifiable lack of a standardized method to evaluate and compare algorithms. Researchers employ differing techniques and assumptions in their development of new solutions, making direct comparison difficult. Through a literature review and a proof-of-concept experiment, this paper aims to assess the feasibility of creating a standardized evaluation protocol for non-stationary preference learning algorithms. We found that the current evaluation landscape is fragmented and existing results may not be representative of real-world performance. We proposed a novel usability metric called the annoyance metric and conducted an experiment evaluating the feasibility of using an LLM as a proxy for human feedback and conclude that it is not a practical evaluation solution. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Business & IT BSc (56066) |
| Link to this item: | https://purl.utwente.nl/essays/107865 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
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