The good lie: humans steering interactive AI’s behaviour to reach a common goal

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Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu | Master's thesis
Date
2021-05-17
Department
Major/Subject
Machine Learning, Data Science and Artificial Intelligence (Macadamia)
Mcode
SCI3044
Degree programme
Master’s Programme in Computer, Communication and Information Sciences
Language
en
Pages
69+10
Series
Abstract
The importance of human interaction in machine learning is nowadays becoming more and more recognised. While automatic Machine Learning is undoubtedly powerful, increasing evidence is showing that many scenarios require or benefit from interaction with a human expert. This work focuses on interactive optimisation, in a setting where the user and the system need to collaborate to reach a common goal. The system is an optimiser that tries to find the maximum of a function, but cannot directly access the function. In fact, it needs to ask the user, who can see the function entirely, the function value at each point. This scenario was chosen as it mimics the dynamics of real interactive recommender systems, but avoids its complexity, hence rendering it a flexible model to approximate many similar situations of this kind. Not only it will be shown that most users, after learning a model of the system, start providing feedback different from the true value; but also that such behaviour is beneficial in reaching the common objective. Besides, we will discuss how to model users to capture and predict such behaviour in order to reach the maximum even earlier.
Description
Supervisor
Kaski, Samuel
Thesis advisor
Daee, Pedram
Keywords
Bayesian optimisation, Gaussian processes, Human-in-the-loop machine learning, interactive machine learning, personalised information retrieval, user modelling
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