# Intelligence via ultrafilters: structural properties of some intelligence comparators of deterministic Legg-Hutter agents

arXiv: https://arxiv.org/abs/1910.09721 · HTML: https://mlsanity.com/paper/1910.09721/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2019.

## Built on by (most cited first, 4 of 4)

- [Measuring Intelligence and Growth Rate: Variations on Hibbard's Intelligence Measure](https://mlsanity.com/paper/2101.12047/) (2021)
- [Reward-Punishment Symmetric Universal Intelligence](https://mlsanity.com/paper/2110.02450/) (2021)
- [Extending Environments To Measure Self-Reflection In Reinforcement Learning](https://mlsanity.com/paper/2110.06890/) (2021)
- [Representation and Invariance in Reinforcement Learning](https://mlsanity.com/paper/2112.07752/) (2021)

## Abstract (arXiv)

Legg and Hutter, as well as subsequent authors, considered intelligent agents through the lens of interaction with reward-giving environments, attempting to assign numeric intelligence measures to such agents, with the guiding principle that a more intelligent agent should gain higher rewards from environments in some aggregate sense. In this paper, we consider a related question: rather than measure numeric intelligence of one Legg- Hutter agent, how can we compare the relative intelligence of two Legg-Hutter agents? We propose an elegant answer based on the following insight: we can view Legg-Hutter agents as candidates in an election, whose voters are environments, letting each environment vote (via its rewards) which agent (if either) is more intelligent. This leads to an abstract family of comparators simple enough that we can prove some structural theorems about them. It is an open question whether these structural theorems apply to more practical intelligence measures.