Expect the unexpected: Implementing mental models as Markov chains to predict unexpected events
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2023-06-12
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en
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Abstract
Technological advancements are causing humans to increasingly work together with
artificial agents [1]. Explainable AI (XAI) is an important and necessary approach when
interacting with artificial agents. Information on the expectations of humans can be used
to inform better explanations [2]. These expectations humans have of other agents and
the world can be formalised as a mental model. This study investigates the possibility of
using approximated mental models for predicting unexpected events.
An experiment with a Human-Robot Interaction (HRI) element was used to create a
dataset with expected and unexpected events. A nontrivial cooperative card game was
used as the experiment task. To approximate the mental model of the human, synthetic
data was generated using Monte-Carlo Tree Search (MCTS) agents. Next, Markov Chains
(MCs) were constructed for this task and trained on this synthetic data. After the initial
results, an additional model trained on human data was developed. This was done to
check if human data better approximates human mental models. The model trained on
synthetic data performed at the chance level (weighted F1: 0.53) while the model trained
on human data had moderately positive results (weighted F1: 0.65). Both models showed
highly differing results per participant, with the initial model trained on synthetic data
showing the most extreme values.
The project is a first exploration into the use of Markov models as a MH representation
in a complex HRI task to predict unexpected events. Results show that better approximations
of MH do result in overall better performance in predicting unexpected events.
However, the performance varied considerably between individual participants for both
models. For the model developed in this thesis, a classification threshold hyperparameter
was identified and its effect on model performance was explored and found to have a very
strong infleunce.
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Faculteit der Sociale Wetenschappen