Intervention Complexity as a Canonical Reward and a Measure of Intelligence
The Legg--Hutter universal intelligence measure provides a rigorous scalar assessment of general intelligence as expected reward across all computable environments, weighted by simplicity. However, the measure presupposes an externally specified reward function, raising the question of whether the reward primitive is inherently arbitrary or whether a canonical choice exists. We propose a new measure, called intervention complexity, that has five natural properties: environment-derivedness, universality, minimality, sensitivity, and achievement preference. Given a resource function rho encoding an inductive bias (such as program length, execution time, or energy), rho-intervention complexi...
| Authors | Brendan McCane |
|---|---|
| Primary category | cs.AI |
| Published | 4 May 2026 |
| Last updated at source | 9 May 2026 |
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