Researchers have introduced a new measure of intelligence called intervention complexity, which aims to provide a canonical reward function within the Legg-Hutter universal intelligence framework. This measure is derived from the environment itself and is indexed by a resource bias, such as program length or execution time, offering a principled way to assess intelligence without external normative input. The framework also proposes a two-dimensional characterization of intelligence focusing on agent competence and learning efficiency, with the choice of resource bias influencing the computability of the measure. AI
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IMPACT Introduces a new theoretical framework for intelligence measurement, potentially impacting future AI development and evaluation.
RANK_REASON This is a research paper published on arXiv proposing a new theoretical framework for measuring intelligence. [lever_c_demoted from research: ic=1 ai=1.0]