Abstract:

Why did I write this thesis? There are several possible goals that one
can come up with to explain my behavior, e.g. earning a bachelor degree
or trying to publish. Explaining observed behavior with the goal causing
that behavior is called goal inference. The best explanation will be inferred,
from several possible goals, through a process of Inference to the Best Ex-
planation (IBE). In the cognitive (neuro-)science literature goal inference
is often modeled using probabilistic IBE. In this thesis two probabilistic
approaches of IBE, Maximum Likelihood (ML) and Most Probable Explanation (MPE), are evaluated. Glass (2007) applied ML and MPE to medical
diagnostic cases and demonstrated that these approaches have some problematic aspects. ML ignores prior probabilities and MPE overweighs them.
The question I tried to answer was whether these problems generalize to
the domain of goal inference. The approach taken was analogous to the
approach of Glass. The predictions of the ML and MPE models in three
scenarios of goal inference were compared to human intuitions. It turned out
that the problem of the ML model was present in two of the three scenarios.
This suggests that the problem of ML does generalize to the domain of goal
inference. The predictions of the MPE model matched human intuition in
all three scenarios. This result suggests that the problem of MPE does not
generalize.