# Value Under Ignorance in Universal Artificial Intelligence

arXiv: https://arxiv.org/abs/2512.17086 · HTML: https://mlsanity.com/paper/2512.17086/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods.
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 18 Dec 2025.

## Placed in

- Deep reinforcement learning methods / [RL theory and sample complexity](https://mlsanity.com/deeprl/rl-theory-and-sample-complexity/)

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

- [Settling the Reward Hypothesis](https://mlsanity.com/paper/2212.10420/) (2022)
- [Thinking Machines: A Survey of LLM based Reasoning Strategies](https://mlsanity.com/paper/2503.10814/) (2025)
- [Does RLHF Scale? Exploring the Impacts From Data, Model, and Method](https://mlsanity.com/paper/2412.06000/) (2024)
- [Death and Suicide in Universal Artificial Intelligence](https://mlsanity.com/paper/1606.00652/) (2016)
- [Regret Bounds for Robust Online Decision Making](https://mlsanity.com/paper/2504.06820/) (2025)

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

- [Solomonoff induction](https://mlsanity.com/paper/2603.20274/) (2026)
- [Golden Handcuffs make safer AI agents](https://mlsanity.com/paper/2604.13609/) (2026)
- [Understanding Generalization Requires Universal Induction](https://mlsanity.com/paper/2609.34458/) (2026)

## Abstract (arXiv)

We generalize the AIXI reinforcement learning agent to admit a wider class of utility functions. Assigning a utility to each possible interaction history forces us to confront the ambiguity that some hypotheses in the agent's belief distribution only predict a finite prefix of the history, which is sometimes interpreted as implying a chance of death equal to a quantity called the semimeasure loss. This death interpretation suggests one way to assign utilities to such history prefixes. We argue that it is as natural to view the belief distributions as imprecise probability distributions, with the semimeasure loss as total ignorance. This motivates us to consider the consequences of computing expected utilities with Choquet integrals from imprecise probability theory, including an investigation of their computability level. We recover the standard recursive value function as a special case. However, our most general expected utilities under the death interpretation cannot be characterized as such Choquet integrals.