# Reward-Punishment Symmetric Universal Intelligence

arXiv: https://arxiv.org/abs/2110.02450 · HTML: https://mlsanity.com/paper/2110.02450/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 6 Oct 2021.

## Builds on (most cited first, 5 of 5)

- [Bad Universal Priors and Notions of Optimality](https://mlsanity.com/paper/1510.04931/) (2015)
- [Death and Suicide in Universal Artificial Intelligence](https://mlsanity.com/paper/1606.00652/) (2016)
- [The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI](https://mlsanity.com/paper/2002.10221/) (2020)
- [Intelligence via ultrafilters: structural properties of some intelligence comparators of deterministic Legg-Hutter agents](https://mlsanity.com/paper/1910.09721/) (2019)
- [Measuring Intelligence and Growth Rate: Variations on Hibbard's Intelligence Measure](https://mlsanity.com/paper/2101.12047/) (2021)

## Built on by (most cited first, 3 of 3)

- [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)
- [Universal Agent Mixtures and the Geometry of Intelligence](https://mlsanity.com/paper/2302.06083/) (2023)

## Abstract (arXiv)

Can an agent's intelligence level be negative? We extend the Legg-Hutter agent-environment framework to include punishments and argue for an affirmative answer to that question. We show that if the background encodings and Universal Turing Machine (UTM) admit certain Kolmogorov complexity symmetries, then the resulting Legg-Hutter intelligence measure is symmetric about the origin. In particular, this implies reward-ignoring agents have Legg-Hutter intelligence 0 according to such UTMs.