@inproceedings{
macdermott2024measuring,
title={Measuring Goal-Directedness},
author={Matt MacDermott and James Fox and Francesco Belardinelli and Tom Everitt},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=o4coDIby7e}
}
We define maximum entropy goal-directedness (MEG), a formal measure of goal-
directedness in causal models and Markov decision processes, and give algorithms
for computing it. Measuring goal-directedness is important, as it is a critical
element of many concerns about harm from AI. It is also of philosophical interest,
as goal-directedness is a key aspect of agency. MEG is based on an adaptation of
the maximum causal entropy framework used in inverse reinforcement learning. It
can measure goal-directedness with respect to a known utility function, a hypothesis
class of utility functions, or a set of random variables. We prove that MEG satisfies
several desiderata and demonstrate our algorithms with small-scale experiments.
Measuring Goal-Directedness
Matt MacDermott James Fox Francesco Belardinelli Tom Everitt